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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Instrumentation Amplifier01:25

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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An effective ECG signal compression algorithm with self controlled reconstruction quality.

Hardev Singh Pal1, A Kumar1, Amit Vishwakarma1

  • 1Department of Electronics and Communication Engineering, PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur, Madhya Pradesh, India.

Computer Methods in Biomechanics and Biomedical Engineering
|May 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel electrocardiogram (ECG) compression algorithm using tunable-Q wavelet transform (TQWT) optimized by the coronavirus herd immunity optimizer (CHIO). This method efficiently reduces ECG data size for telehealth, improving storage and transmission.

Keywords:
CHIOECG signal compressionoptimizationtunable-Q wavelet transform

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Continuous electrocardiogram (ECG) monitoring generates large datasets, posing challenges for telehealth storage and transmission.
  • Efficient compression of ECG signals is crucial for effective remote patient monitoring and data management.

Purpose of the Study:

  • To propose a novel and efficient ECG compression algorithm.
  • To integrate tunable-Q wavelet transform (TQWT) with the coronavirus herd immunity optimizer (CHIO) for ECG data compression.
  • To enable self-adaptive reconstruction quality regulation via an error parameter.

Main Methods:

  • The proposed algorithm integrates tunable-Q wavelet transform (TQWT) with the coronavirus herd immunity optimizer (CHIO).
  • CHIO is employed to optimize TQWT parameters, including the decomposition level, a novel application in ECG compression.
  • Transform coefficients undergo thresholding, quantization, and encoding for enhanced compression.

Main Results:

  • The algorithm was evaluated on the MIT-BIH arrhythmia database.
  • Performance was benchmarked against established optimization algorithms.
  • Compression metrics included compression ratio, signal-to-noise ratio, percent root mean square difference, quality score, and correlation coefficient.

Conclusions:

  • The novel TQWT-CHIO algorithm offers an efficient approach to ECG signal compression.
  • The self-adaptive feature allows for controlled reconstruction quality.
  • This method shows promise for improving telehealth applications by reducing ECG data size.