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Recognition of positive and negative valence states in children with autism spectrum disorder (ASD) using discrete wavelet transform (DWT) analysis of electrocardiogram signals (ECG)

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 405-416页

作者： Bagirathan, Anandhi Selvaraj, Jerritta Gurusamy, Anusuya Das, Himangshu Vels Inst Sci Technol & Adv Studies VISTAS Chennai Tamil Nadu India Natl Inst Empowerment Persons Multiple Disabil Di Chennai Tamil Nadu India

Children with autism spectrum disorder (ASD) are deficit in communication, social skills, empathy, emotional responsiveness and have significant behavioral pattern. They have difficulty in understanding other feelings and their own emotions. This leads to the sudden emotional outburst and aggressive behavior in these children. Parents, caretakers and doctors find it very difficult to prevent such extreme behaviors. Learning the positive and negative valence leads in determining the early indications before the onset of emotional outbursts in children with ASD. The present study measures the psycho physiological electrocardiogram (ECG) signal from the typically developed (TD) children and children with ASD in the age group of 5-11 years. Personalized protocol was developed for every child with ASD to induce positive and negative valence and ECG data was collected using wearable Shimmer ECG device. The heart rate variability (HRV) and the QRS amplitude were derived from ECG signal using pan-tompkins algorithm and eleven features were extracted using DWT (db2, db4 and db8) mother wavelet. The significant features of ECG, HRV and QRS amplitude were classified using the K nearest neighbor (KNN), support vector machine (SVM) and ensemble classifier. Ensemble and KNN classifier achieved maximum accuracy of 81% and 76.2% for children with ASD and Ensemble and SVM classifiers obtained maximum accuracy of 87.4% and 83.8% for TD children using HRV data.

关键词： Autism spectrum disorder (ASD) Heart rate variability (HRV) pan-tompkins algorithm K nearest neighbor (KNN)

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Quantum leap in cardiac prognosis: EMIP-cardioPPG's pioneering approach to early myocardial infarction prediction

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第12期18卷 8723-8737页

作者： Shrivastava, Abhishek Kumar, Santosh Naik, N. Srinivas IIIT Naya CSE Sect 24 Raipur 493661 Chhattisgarh India IIITDM Kurnool CSE Jagannathagattu Hill Kurnool 518007 Andhra Pradesh India

Cardiovascular disease encompasses conditions affecting the heart and blood vessels, leading to ailments such as coronary artery disease, heart failure, and myocardial infarction (MI), which occurs when blood flow to the heart is obstructed. Early prediction of MI is vital to prevent severe damage and enhance survival rates. Traditionally, an electrocardiogram (ECG) is employed to detect cardiovascular anomalies, but its requirement for multiple electrodes placed on various body locations makes continuous monitoring difficult. Current research gaps involve the necessity for medical assistance during ECG monitoring, data variability, accuracy, early symptom prediction, and limited data availability due to the sensitive nature of medical records. To address these issues, introduce EMIP-CardioPPG, a novel mathematical framework for early MI prediction using CardioPPG, a non-invasive method that utilizes photoplethysmography (PPG) signals to monitor heart rate (HR) and detect cardiovascular abnormalities. Our approach comprises four steps: first, acquiring data from the same individual using two different sources, a self-created IoMT device and a 4-channel BIOPAC-Mp-36 device;second, preprocessing the data by denoising, filtering, normalizing, and removing motion artifacts;and third, employing mathematical calculations to determine heart rate variability (HRV) and HR, enhancing PPG signal features for early MI prediction. Fourth, evaluate our model performance using machine learning algorithms such as ridge regression, support vector classifier, independent component analysis, singular value decomposition, random forest, and XGBoost, pan-tompkins algorithm achieving overall accuracy of 97.91% for HRV from our IomT device and 98.83% for HR from our BIOPAC-MP-36.

关键词： Myocardial infarction Heart rate variability Electrocardiogram Photoplethysmogram Internet of medical things pan-tompkins algorithm

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Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection

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JOURNAL OF WEB ENGINEERING 2020年第5-6期19卷 747-773页

作者： pandey, Saswat Raj Hicks, David Goyal, Ayush Gaurav, Devottam Tiwari, Sanju Mishra Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX USA IIT Delhi Dept Comp Sci & Engn New Delhi India Univ Autonoma Tamaulipas Cd Victoria Tamps Mexico

In today's fast-paced world where patients may need to be remotely monitored while they are away or out of hospitals, there is a need for mobile applications that can gather the biometric and biomedical signals from any number of devices and sensors, collecting biometric or biomedical data from a patient. This effort boons the improvement of a circulatory strain and heartbeat anomaly detection and notice apparatus as an Android application that permits quick discovery of any variations from the norm in a patient's fundamental dependent on the pan-tompkins algorithm and reports it to the pertinent emergency clinic or clinical staff. The blood ECG information can be gotten from the health tracker sensors by means of a Bluetooth association and the patient can enter their Blood pressure esteems. In this case, the information gathered from a set of reproduced data, which is identified with a triggering notice from Firebase Cloud function. This notification is further acknowledged by the enrolled specialist. The system's security part is represented by the fine-grained consent procedure which directs that solitary significant authorizations are required for the best possible working of the application which ought to be given to the application. An encryption method using the Blowfish algorithm is included as a feature of the developed mobile Android application to provide secure data transfer of the patient's vital signals.

关键词： Anomaly detection pan-tompkins algorithm Firebase cloud messaging service diastolic blood pressure systolic blood pressure ECG

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Development of a Concept Demonstrator for QRS Complex Detection using Combined algorithms

Development of a Concept Demonstrator for QRS Complex Detect...

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2nd IEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES)

作者： Ahmad, Ida Laila binti Mohamed, Masnani binti Ab Ghani, Norul Ain binti Univ Tun Hussein Onn Malaysia UTHM Dept Elect Engn Batu Pahat Johor Malaysia

ISBN: (纸本)9781467316668;9781467316644

This paper describes a study on combined algorithms used for classification of QRS Complex in ECG signals. The proposed algorithm/detector uses Hillbert transform on a Wavelet base for the pre-processing stage. Both Hillbert transform and Wavelet base known to be superior in reducing unwanted noise resembles in ECG signal such as baseline wander and muscle noise. In addition, the pan-tompkins algorithm was employed as the QRS peak detection. A testing against MIT-BIH Arrhythmias Database results in a reliable detection error rate (DER) of 98.7%. It can be concluded that, the proposed method offers significant noise reduction in pre-processing stage and still produce a reliable results even with the contaminated ECG signal.

关键词： component ECG QRS Complex Hillbert transform Wavelet pan-tompkins algorithm

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A Two-Stage Feature Extraction Approach for ECG Signals 3rd

A Two-Stage Feature Extraction Approach for ECG Signals

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3rd International Afro-European Conference for Industrial Advancement (AECIA)

作者： Houssein, Essam H. Kilany, Moataz Hassanien, Aboul Ella Snasel, Vaclav Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Informat Cairo Egypt VSB TU Ostrava Dept CS & IT4Innovat Ostrava Czech Republic Sci Res Grp Egypt SRGE Cairo Egypt

ISBN: (纸本)9783319608341;9783319608334

This paper investigate various techniques of extracting features from the electrocardiogram (ECG) signal in order to analyze the ECG signals to detect the heart disease. Feature extraction, is a one of the widespread process of decompose the ECG data. This paper introduce a two-stage feature extraction approach to extract features from ECG signals for different types of arrhythmias. Firstly, Modified pan-Tomkins algorithm (MPTA) is implemented to remove noise and extract nine features. Then the proposed Improved Feature Extraction algorithm (IFEA) is applied to extract additionally ten different features from the ECG signal. The MIT-BIH arrhythmia database have been used to test the proposed approach. It is obvious from the results that the proposed approach shows a high classification in terms of the following four statistical measures: Accuracy (Ac) 98.37%, Recall 48.29%, Precision 43.91%, F Measure 45.31%, and Specificity (Sp) 93.30%, respectively.

关键词： ECG Feature extraction Wavelet transform pan-tompkins algorithm

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Arrhythmia Detection from Heartbeat Using k-Nearest Neighbor Classifier

Arrhythmia Detection from Heartbeat Using <i>k</i>-Nearest N...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Park, Juyoung Lee, Kuyeon Kang, Kyungtae Hanyang Univ Dept Comp Sci & Engn Ansan South Korea

ISBN: (纸本)9781479913091;9781479913107

Automatic interpretation of electrocardiography provides a non-invasive and inexpensive technique to analyze the heart activity for different cardiac conditions. The emergence of smartphones and wireless networks has made it possible to perform continuous Holter monitoring on patients or potential patients. Recently, much attention has been paid to the development of the monitoring methodologies of heart activity, which include both the detection of heartbeats in electrocardiography and the classification of types of heartbeats. However, many studies have focused on classifying limited types of heartbeats. We propose a system for classification into 17 types of heartbeats. This system consists of two parts, the detection and classification of heartbeats. The system detects heartbeats through repetitive features and classifies them using a k-nearest neighbor algorithm. Features such as the QRS complex and P wave were accurately extracted using the pan-tompkins algorithm. For the classifier, the distance metric is an adaptation of locally weighted regression. The system was validated with the MIT-BIH Arrhythmia Database. The system achieved a sensitivity of 97.22% and a specificity of 97.4% for heartbeat detection. The system also achieved a sensitivity of 97.1% and a specificity of 96.9% for classification.

关键词： Arrhythmia detection classifier k-nearest neighbor locally weighted regression ECG QRS complex pan-tompkins algorithm

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Automatic QRS Onset Detection of ECG Signal using Secant Line Slope Formula 15

Automatic QRS Onset Detection of ECG Signal using Secant Lin...

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IEEE 15th International Colloquium on Signal Processing and its Applications (CSPA)

作者： Jumahat, Shaliza Beng, Gan Kok Misran, Norbahiah Islam, Mohammad Tariqul Mahri, Nurhafizah Univ Kebangsaan Malaysia Fac Engn & Built Environm Ctr Adv Elect & Commun Engn Bangi 43000 Selangor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43000 Selangor Malaysia

ISBN: (纸本)9781538675632

In automatic electrocardiogram (ECG) signal analysis, the QRS onset must be identified prior to QT interval or QRS duration measurements. These measurements are decisive ECG parameters for diagnosing cardiac abnormalities among cardiologists. Hence, the efficiency of the developed automatic algorithm to detect the QRS onset is essential to obtain an accurate result of the ECG parameters. In this report, an algorithm to detect the QRS onset based on secant line slope formula is proposed. The preprocessing and wave delineation process were implemented in MATLAB using modified pan-tompkins algorithm (an established adaptive threshold method). The window of the preceding Q-wave was determined before calculating the slope of secant line along the descending slope for QRS onset detection. The performance of the proposed algorithm was evaluated using 25 subjects from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) and volunteered participants under the approval of Research and Ethics Committee, PPUKM (Code of ethics approval: FF-2013-313). All data were acquired using biosignal amplifier (*** by ***, Austria) with 2 minutes duration of recording and sampled at 512 Hz. The efficiency of the proposed algorithm has obtained a sensitivity of 99.67%, positive predictivity of 99.39%, and accuracy of 99.07%. The result shows stable performance and insensitivity of the proposed algorithm towards ECG wave morphology changes.

关键词： automatic ECG detection pan-tompkins algorithm QRS onset secant line slope

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Classification of ECG signals using Machine Learning Techniques: A Survey 2

Classification of ECG signals using Machine Learning Techniq...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Jambukia, Shweta H. Dabhi, Vipul K. Prajapati, Harshadkumar B. Dharmsinh Desai Univ Dept Informat Technol Nadiad India

ISBN: (纸本)9781467369114

Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are available for ECG classification. Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification. This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues. Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers.

关键词： ECG classification survey preprocessing neural network mit-bih database feature extraction pan-tompkins algorithm

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Improving Energy Efficiency in Medical Edge Devices for ECG Feature Detection via Approximate Computing 5

Improving Energy Efficiency in Medical Edge Devices for ECG ...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Nagatomo, Taiki Sato, Toshinori Fukuoka University Department of Electronics Engineering and Computer Science Graduate School of Engineering Fukuoka Japan Fukuoka University Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka Japan

ISBN: (纸本)9798350314694

Improving the energy efficiency of battery-powered medical edge devices is a challenge. In this study, we explored electrocardiogram (ECG) feature detection using the pan-tompkins algorithm and integrated approximate computing techniques involving addition and multiplication into the process to enhance power efficiency. We adopted eight approximate multipliers from EvoApproxLib along with a Carry-Maskable Adder (CMA). We evaluated the impact of applying approximate computing on feature detection accuracy, as well as its influence on enhancing the power efficiency of the arithmetic circuits. When replacing just one of the arithmetic circuits with its approximate counterpart, the most power-efficient multiplier had dynamic power reduced by 48.3% in multiplications while maintaining a detection accuracy of 99.96%, while the CMA reduced dynamic power reduction by 95.2% while maintaining a detection accuracy 99.4%. When the CMA and approximate multiplication were applied, detection accuracy was maintained at 99.4%. In terms of power efficiency, the multiplier played a predominant role and the overall dynamic power consumption was reduced by 48.7%. © 2023 IEEE.

关键词： approximate computing ECG medical edge devices pan-tompkins algorithm signal processing

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You are touching me! A Monitoring Mechanism for Secure Key Generation

You are touching me! A Monitoring Mechanism for Secure Key G...

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作者： JIM BENGTSSON CHALMERS UNIVERSITY OF TECHNOLOGY

学位级别：硕士

The complexity of the technology we interact with in our day to day life increases, but the effort we want to spend managing these interactions does not. Secure com- munication is one of these interactions. This thesis proposes a design of a security protocol that generates symmetric cryptographic keys which can be used as a basis for secure connection between two people. The creation of the keys is based upon the two biometrics, ECG and EEG, and that the two people are physically touching. A proof of concept which depends on the ECG of two people has been implemented. Given the correct EEG hardware, we argue that this proof of concept could easily be extendable to a full system. Through statistical analysis on ECG measurements and experimentation, supporting algorithms have been implemented. The biggest contribution has been the creation of a touch detection algorithm, which can detect the moment when a person is touched, based on their ECG signal.

关键词： Biometric Electrocardiogram Electroencephalography Inter-Pulse Interval Cryptography Symmetric key pan-tompkins algorithm Continuous Wavelet Transform Microcontroller

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