ECG Digital Signal Processing Applications by Nazmul Akter Shahin.pptx

ECG Digital Signal Processing Applications

1. Topic : ECG Digital Signal Processing Applications. Submitted To: Dr. Tangina Sultana Associate Professor Department of ECE Hajee Mohammad Danesh Science & Technology University, Dinajpur Nazmul Akter Shahin Department of Electronics and Communication Engineering (MS-ECE), ID- 2405141 Hajee Mohammad Danesh Science & Technology University, Dinajpur Course Title : Advance Digital Signal Processing Course Code : ECE-543 Submitted By- Hajee Mohammad Danesh Science & Technology University

2. Introduction to ECG • What is ECG (Electrocardiogram )? • Importance in medical diagnostics • Typical ECG waveform: P, QRS, T waves

3. What is Digital Signal Processing (DSP)? • Definition of DSP • Role of DSP in B iomedical E ngineering • Advantages over analog signal processing

4. Why Use DSP in ECG? • Noise reduction • Feature extraction • Real-time monitoring and analysis

5. ECG Signal Acquisition • ECG leads and electrodes • Signal sampling and digitization • Common hardware used

6. Types of Noise in ECG • Powerline interference • Baseline wander • Muscle artifacts (EMG) • Motion artifacts

7. Preprocessing Techniques • Filtering: low-pass, high-pass, notch • Baseline wander removal • Signal normalization

8. Feature Extraction • Detection of P, QRS, T waves • Heart rate variability (HRV) • P R interval measurement

9. QRS Detection Algorithms Pan-Tompkins algorithm • Wavelet transform methods • Machine learning-based detection

10. Classification of ECG Signals • Arrhythmia detection • Normal vs. abnormal classification • Machine learning

11. Result : Real-time ECG Monitoring Systems • Wearable ECG monitors • Mobile and IoT integration • Edge computing in ECG DSP

12. Future Trends • Prediction of heart attack From ECG signal

13. Summary • Key takeaways • Importance of ECG DSP in healthcare ECG digital signal processing improves heart signal analysis by removing noise and enhancing waveform features. It enables accurate detection of cardiac conditions like arrhythmias using algorithms and machine learning. This technology supports real-time monitoring and is increasingly used in wearable health devices.