Hani Saleh Nourhan Bayasi Baker Mohammad Mohammed Ismail Saleh Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias

Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias

von Hani Saleh Nourhan Bayasi Baker Mohammad Mohammed Ismail

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Beschreibung

This book presents techniques necessary to predict cardiac arrhythmias, long before they occur, based on minimal ECG data. The authors describe the key information needed for automated ECG signal processing, including ECG signal pre-processing, feature extraction and classification. The adaptive and novel ECG processing techniques introduced in this book are highly effective and suitable for real-time implementation on ASICs.



This book presents techniques necessary to predict cardiac arrhythmias, long before they occur, based on minimal ECG data. The authors describe the key information needed for automated ECG signal processing, including ECG signal pre-processing, feature extraction and classification. The adaptive and novel ECG processing techniques introduced in this book are highly effective and suitable for real-time implementation on ASICs.


Provides a full overview of ECG signal processing basics and contemporary advances in the field Introduces a new set of novel ECG signal features for automated ECG signal analysis; Enables readers to invent new ECG signal features and determine if they can be effective in predicting or diagnosing cardiac arrhythmias and related disorders Demonstrates results, supported by silicon validation and real-chip tape-outs

Autor*in

Hani Saleh

Themen in »Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias«

Circuits for Remote healthcare ECG remote processing ECG Data Analysis ECG Signal Processing, Classification and Interpretation Prediction of cardiac arrhythmias prediction of VT/Vf

Stimmen zu »Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias«

Details

ISBN: 9783319639734
Verlag: Springer International Publishing
Erscheinung: 20.10.2017

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