Nadi Sadr

Reducing false arrhythmia alarms in the ICU using multimodal signals and robust QRS detection

This study developed algorithms to decrease the arrhythmia false alarms in the ICU by processing multimodal signals of photoplethysmography (PPG), arterial blood pressure (ABP), and two ECG signals. The goal was to detect the five critical arrhythmias comprising asystole (ASY), extreme bradycardia (EBR), extreme tachycardia (ETC), ventricular tachycardia (VTA), and ventricular flutter or fibrillation (VFB). The different characteristics of the arrhythmias suggested the application of individual signal processing for each alarm and the combination of the algorithms to enhance false alarm detection. Thus, different features and signal processing techniques were used for each arrhythmia type. The ECG signals were first processed to reduce the signal interference. Then, a Hilbert-transform based QRS detector algorithm was utilized to identify the QRS complexes, which were then processed to determine the instantaneous heart rate. The pulsatile signals (PPG and ABP) were processed to discover the pulse onset of beats which were then employed to measure the heart rate. The signal quality index (SQI) of the signals was implemented to verify the integrity of the heart rate information. The overall score obtained by our algorithms in the 2015 Computing in Cardiology Challenge was a score of 74.03% for retrospective and 69.92% for real-time analysis.

Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters

We investigated using heart rate variability (HRV), ECG derived respiration and cardiopulmonary coupling features (CPC) calculated from night-time single lead ECG signals to classify one-minute epochs for the presence or absence of sleep apnoea. We used the 35 training recordings of the M.I.T. Physionet Apnea-ECG database. Performance was assessed with leave-one-record-out cross-validation. The best classification performance was achieved using the CPC features in conjunction with the time-domain based HRV parameters. The cross-validated results on the 17,045 epochs of the dataset were an accuracy of 89.8%, a specificity of 92.9%, a sensitivity of 84.7%, and a kappa value of 0.78. These results are comparable with best results reported on this database.We investigated using heart rate variability (HRV), ECG derived respiration and cardiopulmonary coupling features (CPC) calculated from night-time single lead ECG signals to classify one-minute epochs for the presence or absence of sleep apnoea. We used the 35 training recordings of the M.I.T. Physionet Apnea-ECG database. Performance was assessed with leave-one-record-out cross-validation. The best classification performance was achieved using the CPC features in conjunction with the time-domain based HRV parameters. The cross-validated results on the 17,045 epochs of the dataset were an accuracy of 89.8%, a specificity of 92.9%, a sensitivity of 84.7%, and a kappa value of 0.78. These results are comparable with best results reported on this database.

Reducing false arrhythmia alarms in the ICU by Hilbert QRS detection

In this study, we develop algorithms that reduce the arrhythmia false alarms in the ICU by processing the four signals of Photoplethysmography (PPG), arterial blood pressure (ABP), ECG Lead II, and Augmented right arm ECG. Our algorithms detect five arrhythmias including asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia (VT), and ventricular flutter or fibrillation (VF). Real time algorithm is provided. Our processing proceeded as follows. Firstly, preprocessing was applied to the ECG signals by two median filters in order to remove the baseline wander and high-frequency noise. Then a Hilbert-transform based QRS detector algorithm was used to detect R waves from the ECG signals. Following this, RR intervals were calculated from the available ECG signals. Pulse onset points of the pulsatile signals (PPG and ABP) were also detected and the signal quality index (SQI) of the four signals was measured. The ECG based RR intervals were combined with the pulsatile signal based RR intervals using the algorithms provided by the CinC2015 competition organizers. The combined RR intervals were thresholded at the clinically important values for the five arrhythmias. Template matching was used to detect ventricular tachycardia (VT) and power spectrum of ECG signals and identifying the VF frequency components employed to investigate ventricular fibrillation. Our highest overall result was a 98% True Positive Rate (TPR), 66% True Negative Rate (TNR) with a score of 74.03% for the retrospective algorithm. For the realtime algorithm, we achieved a 98% TPR, 65% TNR and a score of 69.92%.

A Comparison of obstructive sleep apnoea detection using three different ECG derived respiration algorithms

In this paper, three different algorithms (QRS amplitude, PCA and kernel PCA) were applied to the ECG signal to extract information of the respiratory activity. Features were then extracted from the respiratory activity and used to classify sleep apnoea episodes using an Extreme Learning Machine classifier. Data from the first 60 minutes of the 35 ECG signal recordings from the MIT PhysioNet Apnea-ECG database was used throughout the study. Performance was measured with leave-on-record-out cross validation. The fan-out number for the ELM classifier was varied between one and ten. The results showed that the performance of the PCA algorithm was equal to or outscored the other two algorithms at all fan-out numbers we explored. Its highest performance was an accuracy of 79.4%, a sensitivity of 48.8%, and a specificity of 87.7% at a fan-out of ten.

A fast approximation method for principal component analysis applied to ECG derived respiration for OSA detection

In this paper, we present an approximation method for principal component analysis (PCA) and apply it to estimating the respiration from the overnight ECG signal. The approximation method is computationally fast with low memory requirements. We compare it to a full PCA method which is applied to segments of the ECG. Features were calculated from the two ECG derived respiration signals (EDR) and classifiers trained to detect obstructive sleep apnoea (OSA). The Extreme Learning Machine and Linear Discriminant classifier were used to classify the recordings. The data from 35 overnight ECG recordings from MIT PhysioNet Apnea-ECG training database was utilized in the paper. Apnoea detection was evaluated with leave-one-record-out cross validation. The approximated PCA method obtained the highest accuracy of 76.4% by ELM classifier at fan-out 10 and accuracy of 78.4% by LDA. While, the segmented PCA achieved lower accuracies for both classifiers, 75.9% by ELM classifier and 76.6% by LDA. We conclude that the approximation method for PCA is well suited to deriving the respiration signal from overnight ECGs.In this paper, we present an approximation method for principal component analysis (PCA) and apply it to estimating the respiration from the overnight ECG signal. The approximation method is computationally fast with low memory requirements. We compare it to a full PCA method which is applied to segments of the ECG. Features were calculated from the two ECG derived respiration signals (EDR) and classifiers trained to detect obstructive sleep apnoea (OSA). The Extreme Learning Machine and Linear Discriminant classifier were used to classify the recordings. The data from 35 overnight ECG recordings from MIT PhysioNet Apnea-ECG training database was utilized in the paper. Apnoea detection was evaluated with leave-one-record-out cross validation. The approximated PCA method obtained the highest accuracy of 76.4% by ELM classifier at fan-out 10 and accuracy of 78.4% by LDA. While, the segmented PCA achieved lower accuracies for both classifiers, 75.9% by ELM classifier and 76.6% by LDA. We conclude that the approximation method for PCA is well suited to deriving the respiration signal from overnight ECGs.

An ECG oximetry system for identifying obstructive and central apnoea events

An automatic algorithm for processing simultaneously acquired electrocardiogram (ECG) and oximetry signals that identifies epochs of pure central apnoea, epochs containing obstructive apnoea and epochs of normal breathing is presented. The algorithm uses time and spectral features from the ECG derived heart-rate and respiration information, as well as features capturing desaturations from the oximeter sensor. Evaluation of performance of the system was achieved by using leave-one-record-out cross validation on the St. Vincents University Hospital / University College Dublin Sleep Apnea Database from the Physionet collections of recorded physiologic signals. When classifying the three epoch types, our system achieved a specificity of 80%, a sensitivity to central apnoea of 44% and sensitivity to obstructive apnoea of 35%. A sensitivity of 81% was achieved when the central and obstructive epochs were combined into one class.

A low-complexity algorithm for detection of atrial fibrillation using an ECG

OBJECTIVES We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. APPROACH Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. MAIN RESULTS On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. SIGNIFICANCE The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.

Sleep apnoea episodes recognition by a committee of ELM classifiers from ECG signal

This paper describes a system for the recognition of sleep apnoea episodes from ECG signals using a committee of extreme learning machine (ELM) classifiers. RR-interval parameters (heart rate variability) have been used as the identifying features as they are directly affected by sleep apnoea. The MIT PhysioNet Apnea-ECG database was used. A committee of five ELM classifiers has been employed to classify one-minute epochs of ECG into normal or apnoeic epochs. Our results show that the classification performance from the committee of networks was superior to the results of a single ELM classifier for fan-outs from 1 to 100. Classification performance reached a plateau at a fan-out of 10. The maximum accuracy was 82.5% with a sensitivity of 81.9% and a specificity of 82.8%. The results were comparable to other published research with the same input data.