An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Abstract

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

“…In the feature extraction phase, useful morphological [14, 15] , temporal [14, [16] , [17] , [18] , [19] ], frequency-based [20] , [21] , [22] and/or transform-based [23] , [24] , [25] , [26] , [27] , [28] features from ECG waveforms are obtained to improve the distinction between samples. The handcrafted feature extraction step requires domain knowledge and increases computational complexity [29, 30] . The requirement for expertise to select optimal features is a challenge [31] .…”

“…LSTM is a practical approach to analyze time-series data [62] . In the last decade, the LSTM algorithm has been employed for arrhythmia detection [ [30] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] ]. Yildirim [65] proposed a wavelet sequence-based LSTM model to classify ECG signals.…”

“…Gao et al. [30] used an LSTM model with focal loss (FL) to classify eight different heartbeats. The combination of the LSTM and CNN model is commonly used in ECG classification.…”

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

“…S. Saadatnejad, M. Oveisi, and M. Hashemi [40] designed the LSTM-Based ECG classification algorithm for continuous monitoring on personal wearable devices. Junli Gao et al [41] introduced an LSTM network with focal loss (FL) to detect arrhythmia on an imbalanced ECG dataset.…”

Mobile Device ECG Classification using quantized Neural Networks

“…The article of Gao et al [24], implements a Long Short Term Memory (LSTM) neural network to use the timing features in ECG signals, Focal Loss (FL) is used to resolve the imbalance of the MIT-BIH arrhythmia database. The results show that the LSTM network with FL obtains an accuracy of 99.26%.…”