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Classification of Implantable Rotary Blood Pump States with Class Noise Hui-Lee Ooi, Manjeevan Seera, Siew-Cheok Ng, Chee Peng Lim, Chu Kiong Loo, Nigel H. Lovell, Fellow, IEEE, Stephen J. Redmond, Senior Member, IEEE, Einly Lim

Abstract—A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve non-opening (ANO) pump state. In addition to the noise-free data sets, up to 40% class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron (MLP) emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise. Index Terms—classification, implantable rotary blood pump, left ventricular assist device, classifier, ensemble classifier, class noise, mislabeling, pump state classification

I. I NTRODUCTION

H

EART failure, being the leading cause of death in the world, affects an estimated 23 million people worldwide [1]. One of the causes of heart failure is the failure of the left ventricle, as it supplies oxygenated blood to the body [2]. For patients with mild-to-moderate heart failure, therapies such as cardiac resynchronization and neurohormonal blockade have been shown to be beneficial [3]. However, therapies for patients with severe heart failure remain limited. Although heart transplantation remains the surgical treatment of choice for terminal congestive heart failure patients, various factors, including limited number of heart donors and the necessity for life-long immunosuppressive therapy, fuel the need for alternative treatments [4]. Over the years, many ventricular assist devices (VADs) have been developed, including pulsatile VADs [5] and implantable rotary blood pumps (IRBPs) [5]. VADs provide an alternative route to supply oxygen rich blood to the circulatory system to sustain cellular respiration and maintain normal body function. Among the various VADs, IRBPs have been widely used because of their smaller size and therefore relative ease with which they can be implanted. Determination of the optimal speed for an IRBP is crucial to satisfy the varying metabolic demands of the body. Ventricular H.L. Ooi, S.C. Ng and E. Lim are with the Department of Biomedical Engineering, University of Malaya, Malaysia. e-mail: einly [email protected] Manjeevan Seera and C.K. Loo are with the Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. C.P. Lim is with Centre for Intelligent Systems Research, Deakin University, Australia. N. H. Lovell and S. J. Redmond are with the Graduate School of Biomedical Engineering, University of New South Wales, Australia.

ejection (VE) is the preferred pump state when there is a net positive blood flow across the aortic valve and the pump [6], [7]. When the pump speed is increased, the ANO state is reached, where the aortic valve remains closed throughout the entire cardiac cycle [8]. At relatively high pump speeds, ventricular collapse (VC) occurs resulting in an obstruction of the pump inlet cannula as the ventricle walls suck together [9]. The VC pump state is regarded as a detrimental condition as it could possibly inducing arrhythmia, shifting the septum, tricuspidal anastomosis, and dislodgement of thrombi [7]. The ANO pump state poses certain medical risks because it can lead to the formation of a thrombus around the aortic root, aortic stenosis, aortic regurgitation, and aortic fusion [10], [11]. In view of this, continuous and automatic detection of the various pump states is of utmost importance. Despite tremendous effort in the field of pump state detection, [6], [7], [12]–[20], limited studies have focused on identifying the ANO state. The earliest studies by Ayre et al. [8] and Endo et al. [21] proposed the state transition index and the index of motor current amplitude respectively. Bishop et al. [22] proposed the use of a modified Karhunen-Lo`eve transformation using time domain features in the analysis of aortic valve opening. However, these studies have not utilized classifiers and therefore provide no statistical basis for comparison with later studies. More recent studies in identifying ANO pump states have employed the use of classifiers, with the most notable reports in [12], [11] and [23]. In [12], a classification and regression tree (CART) was applied to the investigation of various physiologically significant pump states. While some ANO data were included in their work, the primary focus was on the identification of the VC pump state. Sensitivity/specificity of 100%/100% was achieved, however this was probably due to the limited data for the ANO pump state (i.e., a total of 53 data samples). In [11], time-domain features were calculated from the pump flow waveform and tested with three classification algorithms, namely linear discriminant analysis (LDA), quadratic discriminant function (QDF) and k nearest neighbor (k-NN). The investigation of aortic valve opening covered both experimental data on healthy animals and a numerical model with various hemodynamic alterations [11]. Meanwhile, another study [23] was conducted independently to evaluate the performance of ANO detection using animal experimental data with more variability, including variations in cardiac contractility, systemic vascular resistance and total blood volume. Four different types of classification approaches, namely LDA, logistic regression (LR), MLP and k-NN were compared in

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II. E XPERIMENTAL S ETUP In this section, we first describe the signal acquisition component of the IRBP. This is followed by an explanation of state identification, feature computation, feature selection and classification tools used in this study. The application of noiseinduced data sets to evaluate class noise tolerance capabilities of the various approaches is then presented. A. Signal Acquisition Pump speed signals from four anesthetized, healthy greyhounds implanted with an IRBP were taken from an experimental acquisition setup, as detailed. The speed of the centrifugal pump was controlled by a proportional integral controller with a time constant of about 3.5 ms [26]. The IRBP includes an impeller with four blades [27]. Each blade includes a permanent magnet which interacts with two sets of stator coils to rotate the impeller. The brushless DC motor, formed by the stator coils and the magnet within the impeller, sends six back electromotive force (emf) pulses per full revolution back to the controller, which correlates with the different magnet positions while passing the stator coils [27]. Instantaneous speed of the pump was then computed from the back emf signal [27]. The experiment was first implemented with a speed ramp under healthy conditions, from a pump speed of 1600 rpm

to 3000 rpm, at an increment of 100 rpm [26]. This was then followed by different perturbations to vary cardiac contractility, afterload and preload. Invasive signals, including left ventricular (LVP), left atrial (LAP), aortic (AoP) and pump inlet (InP) pressures, as well as aortic (AoQ) and pump (Qp) flows were recorded throughout the experiments. The noninvasive observers of motor current, supply voltage and instantaneous pump impeller speed were monitored from the pump controller and recorded for analysis. As shown in Fig. 1, with each change in speed set point, a transient period with fluctuations in pump speed could be observed before it settled to a steady state. Data investigated for ANO state detection in this study were taken from the steady state signal. The acquired pump speed signal, with an original sampling rate of 4 kHz, was down-sampled to 200 Hz in accordance with most previous studies [12], [19], [28]–[30]. 2100

Pump Speed (rpm)

the study [23]. Both studies [11], [23] reported that the kNN classifier achieved higher accuracy compared to other classification approaches. Experimental data from a real world application is often subjected to possible corruption during the acquisition and transmission process, therefore affecting correct data interpretation. Particularly, class noise originating from mislabeling the classes (in this case, pump states) in the data may compromise the performance of the classification system [24]. It is desirable for the classifier to be able to perform signal identification effectively and accurately with noise tolerance in order to alleviate class noise effects [25]. This trait would be useful for managing a large number of data acquired for development of an automatic system for the identification of IRBP states. To date, most studies focusing on the occurrence of the ANO pump state [8], [11], [23] have not included the VC pump state in their data set. The presence of the VC pump state would complicate the identification of the ANO pump state, therefore features that are able to differentiate between ANO and VC (apart from VE) are required. With regards to classifiers, only a few, such as k-NN [11], [23], LDA [16], [23], LR [11], [23], QDF [11] and MLP [23], were evaluated in previous works for ANO pump state investigation [16]. Furthermore, none of these studies have assessed the robustness of their classification system in the presence of class noise. In the present study, comprehensive evaluation and class noise robustness analysis of various classifier models is performed, focusing on the identification of ANO from other pump states, i.e., VE and VC, under various operating conditions.

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Fig. 1. Snapshot of a speed ramp signal taken from greyhound data, with fluctuations observed in the speed signal during the transition period from one speed set point to the next.

B. State Identification Invasive signals acquired during the experiments were used as a gold standard in categorizing the data into the three different pump states (VE, ANO, VC). The VE state was typified by left ventricle (LV) ejection during systole, and could be identified when the following conditions occurred: (i) maximum LVP > AoP; (ii) AoQ > 0; (iii) presence of dicrotic notch in the AoP waveform; and (iv) Qp > 0. The ANO state was identified based on: (i) maximum LVP < AoP; (ii) AoQ ≈ 0; and (iii) absence of dicrotic notch in the AoP waveform. Alternatively, VC involves obstruction of the pump inlet cannula due to suction of the LV walls at relatively high pump speeds. In this state, pump flow (Qp) falls to near zero at end systole, with the aortic valve closed at all times (AoQ ≈ 0). A small decrease in aortic pressure (AoP) is observed, with steady near-zero LV pressure (LVP) and negative pump inlet pressure (InP) throughout the cardiac cycle.

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TABLE I D ESCRIPTIONS OF ALL FEATURES USED IN THIS STUDY.

Indices Bas1 Bas2 Bas3 Ran1 Ran2 Ran3 Dir1 Dir2 Dir3 Sta1 Sta2 Sta3 Rms1 Rms2 Rms3 Rmr1 Rmr2 Rmr3 Dur1 Dur2 Dur3 Dur4 Gra1 Gra2 Gra3 Gra4 Gra5 Gra6 Gra7

Descriptions Maximum Minimum Mean Range Lower range Upper range Lower range/range Range/mean Lower range/upper range Standard deviation Skewness Kurtosis Root mean square Maximum/root mean square Minimum/root mean square Root mean and range Maximum/root mean and range Minimum/root mean and range Duration of a cycle Duration of half cycle Duration above min-max threshold Duration above mean-max threshold Gradient Maximum gradient Minimum gradient Absolute gradient difference Gradient difference Maximum gradient change in negative slope Maximum gradient change in positive slope

Formula max(x) min(x) mean(x) Bas1 − Bas2 Bas3 − Bas2 Bas1 − Bas3 Ran2 /Ran1 Ran2 /Bas3 Ran2 /Ran3 p P 2 3 ) /(n − 1) P( (x − Bas 3 3 P(x − Bas3 )4 /(n − 1)Sta41 p (x − Bas3 ) /(n − 1)Sta1 mean(x2 ) Bas1 /Rms1 Bas p 2 /Rms1 (Bas3 )(Ran1 ) Bas1 /Rmr1 Bas2 /Rmr1 Count(x) Count(xhalf ) N um(x > (Bas1 + Bas2 )/2) N um(x > (Bas1 + Bas3 )/2) x(i + 1) − x(i) max(x(i + 1) − x(i)) min(x(i + 1) − x(i)) abs(Gra2 ) − abs(Gra3 ) Gra2 − Gra3 max((xnhalf (i + 1) − xnhalf (i)) − (xnhalf (i) − xnhalf (i − 1))) max((xphalf (i + 1) − xphalf (i)) − (xphalf (i) − xphalf (i − 1)))

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C. Feature Computation

D. Feature Selection In order to effectively reduce the computational burden and time of a real-time recognition system, it is necessary to reduce the size of the feature set while preserving most of the important information required to achieve optimal performance. Feature selection methods can be generally categorized into two classes, i.e., filter and wrapper methods. Filter-based methods evaluate the feature subset using intrinsic characteristics of the data samples [35]. The advantages of filter-based methods include that they are scalable to very highdimensional data sets [36] and are computationally efficient [35]. Another key advantage is that they work independently

filtered signal moving average of filtered signal 2300 2250 Pump Speed (rpm)

Cycle estimation was performed to facilitate the computation of cycle-based features for the classification of pump states. A low-pass filter with a cutoff frequency of 10 Hz was applied to the raw signal as a pre-processing step, followed by a moving average filter. The size for the moving average filter length was set at 200 data points (1 seconds), according to the findings in [23]. The cycle was determined by identifying the successive alternate crossing point between the filtered signal and the moving-averaged filtered signal. Various morphologybased features were then derived and computed. The mean value was calculated for the multiple complete cycles falling in each five second window, as shown in Fig. 2. A total of 29 features, proposed by previous studies, were extracted from the speed signals. These features were grouped into eight different categories (Bas, Ran, Dir, Sta, Rms, Rmr, Dur, Gra). Table I details a complete list of the features with their brief descriptions. Features in the Bas category were derived from basic statistical calculations, such as mean, maximum, and minimum value of a cycle. These features have been previously employed in pump state studies to identify the VC pump state [6], [7], [12], [19], [31]–[33]. Features in the Ran category are related to the signal range and are generated using subtraction operations performed on features from the Bas category. These are included to account for the characteristic change of the pump speed amplitude during pump state transitions. Features in the Dir category are ratio of features in the Ran category. These features take into consideration relative range of the amplitude changes and were previously proposed for distinguishing the ANO state from the VE state [18], [29], [34]. Features in both the Sta (statistical distribution) and Rms (root mean square) categories were proposed for differentiating between the open and closed states of the aortic valve [11], [23] due to their ability to detect irregularities in the morphology of the pump speed waveform. Features in the Rmr category are essentially the derivation and permutation forms of the Rms feature counterparts. Features in the Dur category are duration-based and have been used in previous studies for VC detection due to the assumption that the cycle is symmetrical for a non-suction event [6], [7], [12], [19], [31]– [33]. Features in the Gra category are related to the gradient of the data, previously introduced in [7], [16], [29] to identify the presence of saddles in the waveform during suction events.

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Fig. 2. Cycle estimation for feature computation on five-second segment of pump speed signal.

of the classification algorithms used later. In other words, the selected features can be utilized by a variety of classification algorithms. However, as a result of this independence, the main limitation of filter-based methods is that interactions between the feature subset and the classifier is ignored, which can lead to poor classification performance [36]. Wrapper-based methods use the classifier model during its evaluation of each feature subset [35]. The advantages of wrapper-based methods include that they take into account the ability of a classifier model to approximate or accommodate the distribution of a proposed feature [37], hence the final selected feature subset, when used with the desired classifier, often performs better than features selected by a filter-based method [35]. The main limitation of wrapper-based methods is that they are computationally intensive, which can be an issue when the data set contains a large number of features [38]. Compared with filter-based methods, they are susceptible to a higher risk of over-fitting, or require that additional data is acquired with which to perform final validation of the trained classifier model [36]. In addition, wrapper-based feature selection must be re-run each time a new classifier model is used [38]. In this study, a filter-based method was adopted mainly because the same feature subset selected by the filter-based method can be used to evaluate a number of classification algorithms, in order to have a fair comparison of their performances. In addition, the computational cost of the evaluation process can be minimized by using a filter-based method. A genetic algorithm (GA), a search technique that attempts to mimic the evolution process in nature, was used for feature selection as it is appropriate for finding solutions in highdimensional feature spaces within reasonable amount of time [39]–[41]. Due to its stochastic nature, the GA often performs effectively and is resilient to noise, making it a robust feature selection method [42]. The stopping criteria for GA feature selection is the number of generations, which is fixed at 20 in this study. Crossover probability and mutation probability are 0.6% and 0.033% respectively. We ran feature selection algorithm for a number of the different class noise levels

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before proceeding to the classification task, using the same stopping criterion adopted in the noise-free scenario. E. Classification In order to correctly perform ANO pump state detection, a binary classification task was implemented to distinguish between the ANO and non-ANO pump states. In this context, the non-ANO category consists of both VE and VC pump states. A total of 1,214 cycles of ANO pump state and 1,634 cycles of non-ANO pump state, identified using the approach described in Section II-B were acquired in this study. The Waikato Environment for Knowledge Analysis (WEKA) [43] tool was used to perform the classification task. A total of five classifiers (LR, SVM, k-NN, MLP, CART) and two ensemble classifiers (Bagging (BG) and Random Forest (RF)) were evaluated and compared. SVM classification was performed using the Pearson VII kernel (PUK). MLP used back propagation function with single hidden layer. BG was implemented by utilizing the fast decision learner tree. LR, k-NN, and MLP have been previously used in several ANO pump state studies [20], [23], whereas LR, SVM, MLP and CART were investigated in previous works focusing on the VC pump state [6], [7], [12], [20]. Ensemble classifiers utilize multiple learning algorithms to achieve higher predictive performance. In this study, BG and RF were introduced to evaluate their comparative performances in identifying the ANO pump state. We performed parameter optimization for each classifier we evaluated in the present study at different class noise levels, starting from the default values provided by WEKA, using the grid search method. In order to avoid the risk of overfitting, and considering that a large number of parameters are involved, we have only chosen to tune the most important parameters for each classifier and kept the remaining parameters at their default settings. The best parameter settings for each classifier were identified by optimizing the crossvalidated accuracy on the training data using ten-fold cross validation. Table III shows parameters that have been chosen to be optimized for each classifier, with their corresponding optimized values in the absence of class noise. TABLE II O PTIMIZED PARAMETER SETTINGS FOR EACH CLASSIFIER EVALUATED IN THE PRESENT STUDY WITHOUT CLASS NOISE . ClassifierParameter BG CART k-NN LR MLP RF SVM

Percentage of bagging size Number of iterations Minimal number of observations at terminal node Number of nearest neighbours Ridge value in the log-likelihood Learning rate Momentum applied to the weight during updating The number of epochs to train through The number of trees to be generated The number of attributes to consider Maximum depth of the trees Complexity parameter (c) Epsilon (ǫ)

Optimal value 100 10 2 11 1E-08 0.3 0.2 400 10 0 0 1 1E-12

F. Evaluation Methods The data was divided randomly into a training set and a testing set, with a ratio of 7:3. Optimization of parameter settings for each classifier as well as feature selection was performed using the training set, which was further split into train and validation sets. In order to assess the robustness of the classifiers against class noise, the AddNoise filter in WEKA was used, where the class labels of a random subsample in the train sets were changed. Different levels of class noise, representing the percentage of samples with their respective class attributes mislabeled, was synthetically added (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%) to represent disturbance caused by mislabeling. Ten-fold cross validation was performed with the training set, and the resultant trained model was then assessed using the testing data, which has not been used in the selection of classifier parameters and features. The classification performance for all implemented classification approaches were evaluated on the testing set in terms of sensitivity, specificity as well as the area under the receiver operating characteristic curve (AUC). Sensitivity and specificity indicate the performance of classification in producing correct identification of the ANO pump states and non-ANO pump states, respectively. AUC explicitly considers the trade-off between sensitivity and specificity [44]. III. R ESULTS In order to focus the implementation procedure on the identification of the ANO state, binary classification was performed (ANO vs non-ANO). Using the GA feature selection method, the feature set was reduced to a subset of seven features (Bas1 , Bas2 , Ran2 , Dir1 , Sta3 , Dur2 , Gra7 ) in the absence of class noise. Slightly different subsets of features, as listed in Table III, were selected when different levels of class noise were induced. As shown in Fig. 3, the most frequently selected features across different class noise levels were Bas1 , Bas2 , Dir1 , Gra7 and Dur1 . They were selected at least eight times out of nine (noise-free and noisy) experiments. TABLE III S ELECTED FEATURES WHEN SUBJECTED TO DIFFERENT LEVELS OF CLASS NOISE . Noise (%) 0 5 10 15 20 25 30 35 40

Selected features Bas1 ,Bas2 ,Ran2 ,Dir1 ,Sta3 ,Dur1 ,Gra7 Bas1 ,Bas2 ,Dir1 ,Dir2 ,Sta3 ,Rms1 ,Rmr1 ,Dur1 ,Dur2 ,Gra7 Bas1 ,Bas2 ,Dir1 ,Sta3 ,Rms1 ,Dur1 ,Dur2 ,Dur3 ,Gra3 ,Gra7 Bas1 ,Bas2 ,Ran2 ,Dir1 ,Sta3 ,Rms1 ,Dur1 ,Dur2 ,Gra7 Bas1 ,Bas2 ,Dir1 ,Sta3 ,Rms1 ,Dur1 ,Gra3 ,Gra7 Bas1 ,Bas2 ,Dir1 ,Dir2 ,Sta3 ,Rms1 ,Dur1 ,Dur2 ,Gra7 Bas1 ,Bas2 ,Dir1 ,Dir2 ,Sta2 ,Sta3 ,Dur1 ,Dur2 ,Dur4 ,Gra6 , Gra7 Bas2 ,Dir1 ,Dir3 ,Dur2 ,Dur4 ,Gra7 Bas2 ,Dir1 ,Dir3 ,Sta3 ,Dur1 ,Dur2 ,Gra7

Table IV illustrates the comparative classification performance for the full feature set and reduced feature set without class noise. When the classification test was performed on the

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Bas1 Bas2 Ran2 Dir1 Dir2 Dir3 Sta2 Sta3 Rms1Rmr1Dur1 Dur2 Dur3 Dur4 Gra3 Gra6 Gra7 Feature

Fig. 3. Histogram of selected features created by pooling selected feature subsets across all tested levels of class noise.

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data using the full feature set, it was observed that in a noisefree condition the highest AUC score of 0.995 was achieved by k-NN with 95.9% sensitivity and 97.6% specificity. RF (sensitivity/specificity of 95.5%/97.5%) ranked second with an AUC score of 0.992. Performance for all other classifiers, including BG (94.3%/97.2%), MLP (96.1%/97.3%), SVM (97.3%/99.2%), CART (93.4%/94.0%) and LR (89.2%/93.4%) was reasonable with AUC scores of 0.990, 0.990, 0.980, 0.964, and 0.961, respectively. When the reduced feature set was used, the overall performance ranking among the classifiers remained relatively unchanged. It can be observed that each classifier demonstrated either comparable performance (k-NN, BG, RF) or a very minor deterioration (CART, MLP, SVM) in performance when a reduced number of features were used, which significantly shortened the training time, except for LR which showed a greater percentage of reduction in performance (79.3%/80.5%). Fig. 4 illustrates the AUC scores of each classifier when subjected to different amounts of noise using the reduced feature set. The AUC scores of both k-NN and RF fell rapidly when class noise was encountered (k-NN: from 0.994 in noisefree scenario to 0.671 at 40% noise level; RF: from 0.994 in noise-free scenario to 0.655 at 40% noise level). Performance of BG was fairly superior when class noise of 5% to 30% was induced in the classification, but deteriorated rapidly at 35% (AUC of 0.843) and 40%(0.708) noise levels. For CART, the AUC score was initially maintained without significant change until the addition of 20% class noise (0.900), above which its performance deteriorated continuously and consistently (0.805 at 40% noise level). Meanwhile, SVM and MLP demonstrated lesser degradation in performance in the presence of class noise, with MLP showing slightly superior performance (MLP: from 0.955 in noise-free scenario to 0.910 at 40% noise level; SVM: from 0.956 in noise-free scenario to 0.864 at 40% noise level). LR, on the other hand, despite its subpar performance reasonable compared to its counterparts in the noise-free scenario, showed apparent immunity towards the effect of mislabeling (0.852 at 40% noise level).

BG CART k−NN LR MLP RF SVM

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Fig. 4. Comparative accuracy for reduced feature set when subjected to different amount of noise.

IV. D ISCUSSION Accurate determination of the ANO pump state is crucial for the development of physiologically-responsive pump control strategies to ensure the well-being of patients implanted with an IRBP. It is vital for an automatic ANO detection system to perform signal identification accurately with minimal deterioration in the presence of class noise. Measurement noise and signal drift could result in inaccurate determination of the pumping states during the identification process, which subsequently compromise the performance of the classification system. In spite of that, we are not aware of any studies of ANO pump state classification which assessed the robustness of their classification system in the presence of class noise. Moreover, most studies focusing on the occurrence of the ANO pump state have not included the VC pump state in their data set, which would complicate the identification of the ANO pump state. Therefore, the main focus of the present study is to evaluate the robustness of five classifiers and two ensemble classifiers in the presence of class noise with different features related to ANO occurrence. Overall, our results showed that the reduced feature set produced a minor degradation in the classification performance as compared to the full feature set (Table IV). The most probable reason for this slight deterioration in performance was due to the use of the filter-based feature selection algorithm, which ignores the interactions between the feature subset and the classifiers. In a real-time embedded system, such as an IRBP, with associated timing and hardware constraints [6], an efficient algorithm with a minimal number of features without sacrificing the classification accuracy is required to reduce the computational cost. As suggested by Karantonis et al. [33], both responsiveness and accuracy are clinically important attributes when the classifier is used in an IRBP controller. Besides reducing the processing time, minimizing the usage of features and thus the required number of index threshold settings would substantially reduce the complexity of the system and make it more clinically intuitive [7].

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TABLE IV C OMPARATIVE PERFORMANCE FOR FULL AND REDUCED FEATURE SETS WITHOUT CLASS NOISE . Classifiers k-NN RF MLP BG SVM CART LR

Sensitivity (%) 95.9 95.5 96.1 94.3 97.3 93.4 89.2

All features Specificity (%) 97.6 97.5 97.3 97.2 99.2 94.0 93.4

The reduced feature set acquired from the GA feature selection process in the absence of class noise comprises of Bas1 , Bas2 , Ran2 , Dir1 , Sta3 , Dur1 and Gra7 . Bas1 and Bas2 have been previously used successfully for the detection of the VC pump state [7], [12], [16], [19] based on the close correlation between the pump speed level and the pumping state transitions. Similarly, Dur1 , Ran2 and Gra7 were proposed in [7] for investigating IRBP pump states, with emphasis on the VC pump state. In particular, Gra7 has been shown to outperform other features due to its ability to capture the positive saddle and plateau phenomena frequently observed during a suction event. Meanwhile, Dir1 , which reflects the asymmetry of the speed amplitude during suction, was used to detect the VC pump state [6], [12], [31]–[33]. Among the seven selected features, only Sta3 , proposed by [11], is designed to detect the ANO pump state. This particular feature measures the peakedness of a distribution, which decreases with closure of the aortic valve [11]. By combining the features discussed above, an effective ANO pump state recognition system was achieved in this study, with an AUC score of 0.994 and sensitivity/specificity of 96.5%/97.1% using the k-NN classifier. In comparison, a sensitivity/ specificity of 96.5%/86.8% was reported in [11] while an accuracy of 94.6% was obtained in [23] using the same classifier in the detection of the ANO pump state. When the features from these studies were extracted from data used in the present study, a significant reduction in accuracy occurred (i.e., 89.2% and 80.2%, respectively). We believe that this discrepancy is caused by the lesser variability [11] and absence of suction events [23] in their data. As each of the features varied in terms of distribution, k-NN and ensemble methods (RF and BG) performed well with high AUC scores when performing classification in the absence of class noise. Despite its simplistic nature of classification, k-NN is a highly effective method in classifying the ANO pump state. This is consistent with the findings in [11] and [23] that applied k-NN classifier to ANO detection. On the other hand, the ensemble classifiers (i.e., RF and BG), which have never been used in previous pumping state classification studies, demonstrated superior performance over other classifiers, except for k-NN. As for MLP and SVM, which has been previously applied in [6] and [20], the obtained AUC score was fairly high, at 0.955 and 0.956, respectively. Meanwhile, CART, previously used in several automatic suction detection systems [12], [31]–[33], performed reasonably well in the present study with AUC score of 0.948. Despite being proposed in [7] for identifying the VC pump state and in

AUC 0.995 0.992 0.990 0.990 0.980 0.964 0.961

Reduced features Sensitivity (%) Specificity(%) 96.5 97.1 94.9 98.1 88.5 94.1 93.7 95.6 94.9 97.3 94.2 94.1 79.3 80.5

AUC 0.994 0.994 0.955 0.987 0.956 0.948 0.881

previous works on ANO pump state identification [11], [23], the use of LR for pump state classification in the present study produced relatively poor performance when compared to other classifiers. It is vital for the ANO detection system to be able to perform classification accurately with minimal deterioration in the presence of class noise. Among the different evaluated classifiers, it is shown in Fig. 4 that MLP is a suitable tool for ANO pump state detection. Its AUC score in classification without class noise is sufficiently high at 0.995 while its deterioration with the addition of class noise is not as severe as other high performing classifiers such as k-NN and RF. Overall, it is a stable classifier that can handle training data contaminated with class noise while producing satisfactory results with sufficient accuracy. The extent of class noise influence on the classification performance differs depending on the type of classifier used. k-NN was observed to be highly affected by noise as it went from the best performing classifier to the classifier that displays the steepest drop in performance when class noise was induced. The influence of noise was most prominent on k-NN as this non-parametric classifier was more susceptible to noise in neighboring points. On the contrary, our results show that LR gave the least deterioration when the data was contaminated with class noise of different percentages. This is consistent with findings by [45], [46] that reported the robustness of LR in dealing with class noise contamination. Additionally, the robustness of SVM in dealing with data contaminated with class noise has been presented previously in several works [47], [48]. Meanwhile, the overall performance of BG in the presence of noisy data was fairly robust, as supported by several previous works [49], [50] that illustrated its ability in classifying data with mislabelling. However, its classification performance deteriorated substantially when the level of class noise was increased to 35%. As for RF, its performance in the presence of noisy data was observed to be subpar in the present study despite being noted to be fairly competent in handling class noise in [51]. Comparing the two ensemble classifiers used in the present study, BG was noted to be more effective in handling class noise. While there is no further pruning performed for tree splitting in RF, BG utilizes all attributes and uses minimum variance criterion to split the tree, thereby helping to combat the noise in the data labels. Reliable and accurate determination of the ANO pump state is crucial for the development of physiologically-responsive pump control strategies which could promote myocardial recovery and subsequent weaning of IRBP patients. Although

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preload-based control methods, for example that based on a predetermined pulsatility index level [52], [53], have been commonly used in IRBPs, they have demonstrated a lack of adaptation to varying physiological conditions due to their strong dependency on the pump operating point [54]. In particular, the sensitivity of the LV preload to pump flow is significantly reduced during the VE state, when the native heart contributes a significant proportion of the total cardiac output [55]. Compared to full unloading, recent studies have suggested that partial unloading may be more beneficial for patients with potential for myocardial recovery, as it ensures optimum LV washout [56] and reduces the risk of LV stasis [57] and aortic valve fusion [10]. Ardnt et al. [54] proposed a control strategy which allowed the physicians to choose between two distinct operating points according to patient conditions, i.e.: (i) maximum support to ensure the highest feasible flow rate during early post-operative period; and (ii) medium support with occasional opening of the aortic valve in recovering patients to ensure optimum ventricular washout. V. C ONCLUSION Successful detection of the ANO pump state is imperative to ensure the well-being of patients implanted with an IRBP. In this study, we have evaluated five classifiers and two ensemble classifiers with different features related to ANO occurrence. Using a total of seven features selected by the feature selection process, we achieved an AUC score of 0.955 using the MLP classifier under noise-free classification. Despite the addition of noise, the MLP was observed to be fairly robust in handling class noise compared to other classifiers while consistently yielding a high accuracy. In future, the automatic ANO state detection algorithm will be integrated into our IRBP controller to ensure frequent opening of the aortic valve, particularly in patients with potential for myocardial recovery. ACKNOWLEDGMENT This research is supported partially by Ministry of Higher Education Malaysia (Grant number: UM / HIR (MOHE) / ENG / 50), UMRG Research Subprogram (Project Number RP003D-13ICT) and the Australian Research Council Linkages scheme. R EFERENCES [1] S. Khatibzadeh, F. Farzadfar, J. Oliver, M. Ezzati, and A. Moran, “Worldwide risk factors for heart failure: A systematic review and pooled analysis,” International Journal of Cardiology, vol. 168, no. 2, pp. 1186– 1194, 2013. [2] B. Su, L. Zhong, X.-K. Wang, J.-M. Zhang, R. S. Tan, J. C. Allen, S. K. Tan, S. Kim, and H. L. Leo, “Numerical simulation of patientspecific left ventricular model with both mitral and aortic valves by fsi approach,” Computer Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 474–482, 2014. [3] E. McGee Jr, K. Chorpenning, M. C. Brown, E. Breznock, J. A. LaRose, and D. Tamez, “In vivo evaluation of the heartware mvad pump,” The Journal of Heart and Lung Transplantation, vol. 33, no. 4, pp. 366–371, 2014. [4] O. Frazier, H. A. Khalil, R. J. Benkowski, and W. E. Cohn, “Optimization of axial-pump pressure sensitivity for a continuous-flow total artificial heart,” The Journal of Heart and Lung Transplantation, vol. 29, no. 6, pp. 687–691, 2010.

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Classification of Implantable Rotary Blood Pump States With Class Noise.

A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the s...
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