The main purpose of the present work is to design and implement a prototype ECG system with wireless links for continuous monitoring of the subject for cardiac related problems. The ECG signal acquired from subject is filtered, digitized, and compressed for wireless communication. The proposed system can be extended, upon interfacing with other devices, for continuous monitoring of other vital parameters of the patient.
In automation of the ECG signal analysis, the workload of the medical professionals can be reduced. The automated system provides an alert when critical changes are detected by the system. Concisely stated, ECG of the patient is continuously monitored and deviations from normalcy are detected in real-time. The changes in the ECG could
be due to heart attack, fibrillation or arrhythmias. In case of emergency, data is transmitted to a medical practitioner, who in turn can provide necessary directions to take care of the situation. In this manner, as the problems can be detected as and when they occur, the remedial actions are initiated before the problems become serious.
The complete ECG diagnostic system includes a low power Instrumentation amplifier, filters, ADC, Microcontroller and ZIGBEE modules. MATLAB / LABVIEW are used for signal analysis and classification. These environments are capable of not only collecting, recording, transmitting, and displaying ECG data on a real time basis but also for analyzing the acquired ECG data in order to detect the cardiac abnormalities.
The MIT-BIH database signals were used for validation and evaluation of classification algorithms. In order to reduce the memory requirements for storing the acquired ECG signals, ECG data was compressed. Discrete Cosine Transform (DCT) technique was applied for ECG data compression. Here DCT showed good performance with a Compression Ratio (CR) of 82-90.43% and Percent Root Mean Difference (PRD) of 7.9-0.93. Linear Vector Quantization method (LVQ)
is used for identifying the abnormalities associated with the ECG signal. After training the LVQ process with a reasonable number of samples, the algorithm is used for classifying ECG signals. The techniques used in the present work for ECG signal compression and classification gave better results compared to those found in the literature.
Table of Contents
CHAPTER 1 INTRODUCTION
1.1 ECG and its Importance
1.2 ECG Lead System
1.3 Need for ECG Monitoring System
1.4 Literature Survey
1.5 Methodology
1.6 Organization of thesis
CHAPTER 2 ECG DATA ACQUISITION SYSTEM AND HEART RATE MEASUREMENT
2.1 ECG System Requirements
2.1.1 Data Acquisition Unit
2.1.2 Data Processing Unit
2.1.3 Data Communication Unit
2.1.4 Data Analysis Unit
2.2 ECG Signal Data Acquisition
2.2.1 Electrodes used for ECG Signal Pickup
2.2.1.1 Surface Electrodes
2.2.1.2 Adhesive Electrodes
2.3 Instrumentation Amplifier
2.3.1 Requirements of Instrumentation Amplifier
2.3.2 AD620 Instrumentation Amplifier
2.4 Simple ECG Acquisition System
2.4.1 Filters Used in ECG System
2.4.1.1 Low Pass Filter (LPF)
2.4.1.2 High Pass Filter (HPF)
2.4.1.3 Notch Filter
2.5 Data Processing
2.5.1 ADC0804 Analog to Digital Converter
2.5.2 Serial communication using microcontroller 89C51
2.6 Power Supply
2.7 Data Communication Unit
2.7.1 Communication of Data HyperTerminal
2.7.1.1 Steps involved to setup a new connection using window interface
2.7.1.2 Steps involved in saving incoming data to a text file
2.8 Realization of Sigma Delta ADC using Simulink
2.9 Heart Rate Measurement using LABVIEW
2.9.1 Implementation of LABVIEW for ECG Instrumentation and Analysis
2.9.2 Data Acquisition Module
2.9.3 Amplification Module
2.9.4 Filtering module
2.9.5 QRS Detection and Heart Rate Calculation
CHAPTER 3 ECG COMPRESSION TECHNIQUES
3.1 Performance Evaluation of compression
3.1.1 Compression Measurement
3.1.2 Distortion Measurement
3.2 Data Compression
3.2.1 Direct Data Compression
3.2.2 Transformation Methods
3.3 Compression techniques used in the Proposed Work
3.3.1 Amplitude Zone Time Epoch Coding (AZTEC) Algorithm
3.3.1.1 Line Detection (Horizontal Mode) Procedure
3.3.1.2 Line Processing (Slope Mode) Procedure
3.3.2 Turning Point (TP) Algorithm
3.3.3 Coordinate Reduction Time Encoding System (CORTES) Algorithm
3.3.4 Discrete Cosine Transform (DCT) Algorithm
3.3.5 Fast Fourier Transform (FFT) Algorithm
3.4 Comparison of various Compression Techniques
3.4.1 Conclusions
CHAPTER 4 LINEAR VECTOR QUANTIZATION FOR ECG SIGNAL CLASSIFICATION
4.1 Feature Extraction
4.1.1 QRS Detection
4.1.2 R-R Interval Calculation
4.1.3 ST Segment Measurement
4.1.4 Heart Rate Determination
4.2 Artificial Neural Network
4.2.1 Training the Neural Network
4.2.2 Supervised Training for Neural Network
4.3 Linear Vector Quantization (LVQ)
4.3.1 ECG Signal Data Set
4.3.2 Training Algorithm for ECG Signal Classification
4.3.3 Application of the ECG Signal Analysis using LVQ Method
4.3.4 Classifier Performance
CHAPTER 5 RESULTS OF ECG ACQUISITION, COMPRESSION AND ANALYSIS
5.1 Hardware Implementation for Data Acquisition
5.2 Results of the ECG Compression
5.3 Results of Linear Vector Quantization (LVQ) for ECG Signal Analysis
CHAPTER 6 DISCUSSION ON RESULTS
CHAPTER 7 CONCLUSION AND FUTURE WORK
Research Objectives and Core Topics
The primary research objective is to develop a portable, wireless ECG monitoring system that enables real-time acquisition and classification of cardiac arrhythmias. The study focuses on reducing memory storage and communication bandwidth requirements through efficient signal compression techniques while simultaneously improving diagnostic accuracy using advanced machine learning algorithms.
- Design and implementation of a hardware prototype for continuous ECG monitoring using wireless ZigBee transmission.
- Evaluation and comparative analysis of various ECG signal compression algorithms, including AZTEC, CORTES, and Discrete Cosine Transform (DCT).
- Feature extraction and automatic classification of cardiac abnormalities using Linear Vector Quantization (LVQ) neural networks.
- Development of software-based signal processing using MATLAB and LabVIEW for real-time analysis and arrhythmia detection.
Excerpt from the Book
1.1 ECG AND ITS IMPORTANCE
The heart is a muscular organ responsible for pumping blood through the blood vessels by repeated and rhythmic contractions in human beings. The average human heart, beating at 72 beats per minute, will beat approximately 2.5 billion times during a lifetime (about 66 years). It weighs on average 250g to 300g in females and 300g to 350 g in males.
The function of the right side of the heart is to collect de-oxygenated blood, in the right atrium, from the body (via superior and inferior vena cava) and pump via the right ventricle, into the lungs (pulmonary circulation) through pulmonary valve so that carbon dioxide is exchanged with oxygen. This happens through the passive process of diffusion. The left side collects oxygenated blood from the lungs into the left atrium. From the left atrium the blood moves to the left ventricle which pumps it out to the body (via the aorta). On both sides, the ventricles are thicker and stronger than the atria [2].
The Sino Atrial (SA) node is the natural pacemaker that regulates the cardiac function. The SA node is located at the upper portion of the Right Atrium (RA) and is a collection of specialized electrical cells. SA node generates the pulses at regular intervals that travel through a specialized electrical pathway and stimulates the muscle wall of the four chambers of the heart to contract in a certain sequence or pattern. The upper chambers or atria are first stimulated. This is followed by a slight delay to allow the two atria to empty. Finally, the two ventricles are electrically stimulated to expel the blood into the arteries.
As the SA node fires, each electrical impulse travels through the right and left atria. This electrical activity causes the two upper chambers of the heart to contract. This electrical activity can be recorded from the surface of the body as a "P wave" on the Electro Cardio Gram recording (ECG). The electrical impulse then moves to an area known as the Atria-Ventricular (AV) node.
Summary of Chapters
CHAPTER 1 INTRODUCTION: This chapter introduces the prevalence of heart disease, the importance of continuous ECG monitoring, and outlines the thesis structure.
CHAPTER 2 ECG DATA ACQUISITION SYSTEM AND HEART RATE MEASUREMENT: Covers the hardware design, including instrumentation amplifiers, filter circuits, and the implementation of ZigBee and LabVIEW for data acquisition.
CHAPTER 3 ECG COMPRESSION TECHNIQUES: Details various compression algorithms like AZTEC, CORTES, and DCT, evaluating their performance based on compression ratios and signal reconstruction quality.
CHAPTER 4 LINEAR VECTOR QUANTIZATION FOR ECG SIGNAL CLASSIFICATION: Discusses feature extraction methods and the application of Artificial Neural Networks and LVQ to classify ECG signals into specific cardiac conditions.
CHAPTER 5 RESULTS OF ECG ACQUISITION, COMPRESSION AND ANALYSIS: Presents the experimental data, including hardware performance results and comparative results for different compression and classification algorithms.
CHAPTER 6 DISCUSSION ON RESULTS: Provides an analytical review of the implemented hardware and algorithms, comparing the research findings with existing literature and highlighting system advantages.
CHAPTER 7 CONCLUSION AND FUTURE WORK: Summarizes the study’s findings and suggests future improvements, such as the use of DSP controllers and multichannel analysis.
Keywords
ECG, Arrhythmias, Data Compression, Discrete Cosine Transform, Linear Vector Quantization, Wireless Monitoring, ZigBee, LabVIEW, QRS Detection, Heart Rate, Signal Acquisition, Biomedical Instrumentation, Neural Networks, Telemedicine, Cardiac Abnormalities
Frequently Asked Questions
What is the core focus of this research?
The research focuses on the design and implementation of a portable, wireless ECG monitoring system that enables real-time cardiac abnormality detection, alongside efficient data compression techniques to manage storage requirements.
What are the primary fields of study involved?
The study integrates biomedical engineering, digital signal processing, wireless sensor networks (ZigBee), and machine learning/neural networks for medical diagnosis.
What is the main objective of the thesis?
The primary goal is to build a wearable, continuous monitoring system capable of automatically classifying cardiac arrhythmias in real-time, thereby reducing the burden on clinicians and improving patient turnaround times.
What signal processing methods were utilized?
The system uses instrumentation amplifiers, analog-to-digital converters (ADC), filtering stages, and advanced algorithms such as the Discrete Cosine Transform (DCT) for compression and Linear Vector Quantization (LVQ) for pattern classification.
How is the arrhythmia classification performed?
The classification is performed using a Linear Vector Quantization (LVQ) neural network trained on features like R-R intervals, QRS duration, heart rate, and ST segment slopes derived from the ECG data.
Which compression techniques were evaluated?
The study evaluated AZTEC, Turning Point (TP), CORTES, FFT, and Discrete Cosine Transform (DCT) methods to compare their effectiveness in reducing data size.
How did the author handle high-frequency noise in the ECG data?
The system utilizes a combination of hardware-based filtering (low-pass, high-pass, and notch filters) and adaptive signal processing techniques in the software domain to ensure signal integrity.
What were the performance results of the proposed DCT compression?
The proposed DCT-based technique achieved a significant compression ratio (CR) between 82% and 90.43% with a low Percent Root Mean Difference (PRD) of 0.93 to 7.9, outperforming several other literature-based approaches.
What is the overall classification accuracy achieved by the LVQ method?
The implemented LVQ neural network achieved an overall accuracy of 95.5% in identifying four distinct classes of cardiac abnormalities: Tachycardia, Bradycardia, Premature Ventricular Contraction, and Myocardial Infarction.
How might the system be improved in future studies?
Future work could involve integrating DSP controllers directly into the data acquisition hardware to enable on-device compression, as well as evolving the system from a single-channel to a multi-channel configuration for broader diagnostic insights.
- Quote paper
- Tatiparti Padma (Author), 2014, ECG Monitoring System for Detection of Arrhythmias and Minimization of Storage Requirements Using Compression Techniques, Munich, GRIN Verlag, https://www.grin.com/document/1194504
