Time-Based Compression and Classification of Heartbeats

Abstract

Heart function measured by electrocardiograms (ECG) is crucial for patient care. ECG generated waveforms are used to find patterns of irregularities in cardiac cycles in patients. In many cases, irregularities evolve over an extended period of time that requires continuous monitoring. However, this requires wireless ECG recording devices. These devices consist of an enclosed system that includes electrodes, processing circuitry, and a wireless communication block imposing constraints on area, power, bandwidth, and resolution. In order to provide continuous monitoring of cardiac functions for real-time diagnostics, we propose a methodology that combines compression and analysis of heartbeats. The signal encoding scheme is the time-based integrate and fire sampler. The diagnostics can be performed directly on the samples avoiding reconstruction required by the competing finite rate of innovation and compressed sensing. As an added benefit, our scheme provides an efficient hardware implementation and a compressed representation for the ECG recordings, while still preserving discriminative features. We demonstrate the performance of our approach through a heartbeat classification application consisting of normal and irregular heartbeats known as arrhythmia. Our approach that uses simple features extracted from ECG signals is comparable to results in the published literature.

Feasibility and efficacy of a remote real-time wireless ECG monitoring and stimulation system for management of ventricular arrhythmia in rabbits with myocardial infarction

“…A single ECG monitoring channel occasionally limits the diagnostic capability. Furthermore, beat-to-beat manual ECG analysis is a time-consuming process (20,21) and this system requires integration of an automatic analysis system. The model switch with a wireless signal transceiver, a signal transfer device of the extracorporeal system, should be miniaturized to the size of a mobile phone to enable it to be easily carried by the patient.…”

Classification of AAMI heartbeat classes with an interactive ELM ensemble learning approach

“…Regarding ELM, we search for the best parameters (C and ) according to a 3-fold cross-validation procedure in the ranges [10 −3 1000] and [10 −3 10], respectively. For performance evaluation, we use the standard measures: class sensitivity (Se i ), class positive predictive value (Pp i ), overall accuracy (OA), average sensitivity (Se), and average positive predictive value (Pp) [1][2][3][4][5][6]. In addition, we use the specificity (Sp) measure for the scenarios of VEB (V class versus [N, S, and F]) and SVEB (S class versus [N, V, and F]) [13].…”

“…In recent years, the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation are closely followed as a possible solution for standardization [1][2][3][4][5][6]. Typically, the AAMI standard defines five classes of interest: normal (N), ventricular (V), supraventricular (S), fusion of normal and ventricular (F) and unknown beats (Q).…”

Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction

“…Many techniques to extract ECG signal features have been recently developed. Time-based encodings of ECG recordings have been used to perform linear discriminant analysis to classify normal and irregular heartbeats [1]. Wavelet optimization approach for ECG signal classification has also been developed [6].…”