Patient-Specific Deep Architectural Model for ECG Classification

Abstract

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.

PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

“…Ten-fold cross validation results on MIT-BIH showed that this algorithm achieved a highest overall accuracy of 97.96% using Paul wavelet [17]. However, it has been confirmed that MFSWT can better capture the tiny changes in the frequency domain than CWT [13]. Moreover, the ECG signal data used in our study were all from wearable ECG monitoring equipment so as to ensure clinical applicability.…”

“…MFSWT can efficiently contain the time-frequency information of ECG in the transformed 2-D images, such as P-wave, QRS complex and T-wave, and were successfully applied in the previous studies [13,14]. A bound signal-adaptive frequency slice function (FSF) was introduced in MFSWT, which can realize the adaptive measurement of signal energy distribution at different observation frequencies.…”

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

“…Biel et al's research shows that the variance in different human heartbeats can be very high [88]. Many research works, [31,[89][90][91][92][93], have proven that by using a patient-specific model, the detection algorithms have a higher accuracy than the traditional systems in practical cases.…”

Deep Learning in Cardiology

“…AEs have also been used for arrhythmia detection with MITDB. In their article Luo et al [79] utilized quality assessment to remove low quality heartbeats, two median filters for removing power line noise, high-frequency noise and baseline drift. Then, they used a derivative-based algorithm to detect Rpeaks and time windows to segment each heartbeat.…”