Abstract
This work studies wave detection and lossless data compression of an ECG signal using adaptive slope prediction thresholding and a biorthogonal 3.1 wavelet transform-based filter bank realized using linear phase array structure and achieved an SNR of −53.92 dB. It is found that the combination of ECG signal detection and lossless ECG data compression reduces the false wave detection and increases the ECG data compression ratio, thus facilitating a speedy transmission and efficient bandwidth utilization. The proposed ECG detector realized using an FPGA showed significant improvements in power consumption, area, delay, and switching energy. The proposed methodology can be further extended to analyze various biomedical signals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kumar, A., Kumar, M., Komaragiri, R.S. (2023). Conclusion and Future Work. In: High Performance and Power Efficient Electrocardiogram Detectors. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-5303-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-19-5303-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5302-6
Online ISBN: 978-981-19-5303-3
eBook Packages: EngineeringEngineering (R0)