Key result
The proposed priority-based convolutional auto-encoder achieved an average PRD of 1.83%, SNR of 33 dB, and CR of 35.2%, outperforming the traditional CAE approach for ECG signal compression.
Why the study?
Compression of substantial medical data requires significant storage space and transmission time, but previous compression algorithms lack an upgraded approach to achieve improved ECG signal compression without affecting signal quality.
A novel priority-based convolutional auto-encoder approach improves ECG signal compression efficiency and quality compared to traditional methods, which may benefit IoT-based healthcare monitoring systems.
Answers are grounded in this paper and cite their sources.
May enhance ECG compression in IoT monitoring; extends auto-encoder methods but leaves clinical validation open.
Due to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.
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Mahendran et al. (2020) studied Arrhythmia (ECG signal compression). Priority-based convolutional auto-encoder (PCAE) with multi-scoop notch filter vs. Traditional CAE approach was evaluated on Signal compression performance (PRD, SNR, and CR). The proposed priority-based convolutional auto-encoder achieved an average PRD of 1.83%, SNR of 33 dB, and CR of 35.2%, outperforming the traditional CAE approach for ECG signal compression.
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