ECG Signal Compression and Classification Algorithm With Quad Level Vector for ECG Holter System

Abstract

Abstract-An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with −10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

“…Therefore, prior to deploying any algorithm on modern mobile devices, comprehensive evaluation of the algorithm based on robustness to noise, parameter choice, and numerical efficiency is required to improve the quality of diagnosis with respect to processing time or power consumption. One of the recent studies that confirms this recommendation is done by Hyejung et al [145] who developed a simple algorithm to detect QRS complexes for Holter devices. Their simple algorithm, which consists of bandpass filter followed by multiple thresholds, was faster and more efficient compared to relatively more complex methods [35], [146].…”

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

“…The QSW requires few calculations and low hardware costs because it can be implemented merely by using adders and shift operators. The Quad Level Vector (QLV) algorithm [88] is used with dedicated hardware for ECG monitoring LSI [80], [89]. The QLV is generated using DWT and the adaptive threshold.…”

Spline Activated Neural Network for Classifying Cardiac Arrhythmia

“…It also provided a comparative study of methods proposed by researchers to extract ECG signal features. An ECG signal processing method with Quad Level Vector (QLV) for ECG holter system was proposed by Kim et al (2010) which consists of compression and classification flows. The QLV was proposed for both flows to achieve improved performance with lowcomputation complexity.…”