Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care

Zhou, Haiying; Hou, Kun Mean; Zuo, Decheng

doi:10.4236/wsn.2009.14034

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…Incident-recognition-based home healthcare. Some other studies [60] focus on smart home health care, and they use different machine learning based detection techniques to predict sudden events such as sudden heart attacks. Unlike event detection using sound or images, this event detection requires the use of real-time data (such as human state information).…”

Section: Functionality and Security Enhancements Via Learningmentioning

confidence: 99%

Application of learning algorithms in smart home IoT system security

Mao¹,

Lin²,

Bian³

2018

Mathematical Foundations of Computing

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With the rapid development of Internet of Things (IoT) technologies, smart home systems are getting more and more popular in our daily life. Besides providing convenient functionality and tangible benefits, smart home systems also expose users to security risks. To enhance the functionality and the security, machine learning algorithms play an important role in a smart home ecosystem, e.g., ensuring biotechnology-based authentication and authorization, anomalous detection, etc. On the other side, attackers also treat learning algorithms as a tool, as well as a target, to exploit the security vulnerabilities in smart home systems. In this paper, we unify the system architectures suggested by the mainstream service providers, e.g., Samsung, Google, Apple, etc. Based on our proposed overall smart home system model, we investigate the application of learning algorithms in smart home IoT system security. Our study includes two angles. First, we discussed the functionality and security enhancing methods based on learning mechanisms; second, we described the security threats exposed by employing learning techniques. We also explored the potential solutions that may address the aforementioned security problems.2010 Mathematics Subject Classification. Primary: 58F15, 58F17; Secondary: 53C35.

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Section: Functionality and Security Enhancements Via Learningmentioning

confidence: 99%

Application of learning algorithms in smart home IoT system security

Mao¹,

Lin²,

Bian³

2018

Mathematical Foundations of Computing

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“…Apart from wavelet transformation, many other methods were also used in the past twenty years for feature extraction and noise reduction, e.g. geometric analysis [19], difference operation method [20], dynamic threshold [21], spectral analysis [22], Cumulative Sums of Squares [23] etc. Moreover, researchers showed that the neural networks with different configurations were widely used for the classification of ECG signals [24]- [31].…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

Pandit

Zhang

Aslam

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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This paper presents an investigation into the development of an efficient scheme to detect abnormal beat from lead II Electro Cardio Gram (ECG) signals. Firstly, a fast ECG feature extraction algorithm was proposed which could extract the locations, amplitudes waves and interval from lead II ECG signal. We then created 11 customized features based on the outputs of the feature extraction algorithm. Then, we used these 11 features to train an artificial neural network and an ensemble classifier respectively for detecting the abnormal ECG beats. Three manually annotated databases were used for training and testing our system: MIT-BIH Arrhythmia, QT and European ST-T database availed from Physionet databank. The results showed that for an abnormal beat detection, the neural network classifier had an overall accuracy of 98.73% and the ensemble classifier with AdaBoost had 99.40%. Using time domain processing approach, the proposed scheme reduced overall computational complexity as compared to the existing methods with an aim to deploy on the mobile devices in the future to promote early and instant abnormal ECG beat detection.

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“…Since interpreting different morphological features [4] of ECG signals helps in predicting or detecting abnormalities in the heart, it is important to know each morphological feature [5], because abnormalities in any of these morphological features signify some underlining heart disease [6]. As most of the real-time systems are embedded in small devices, real time computing deals needs to address constraints in terms of memory, computational power, performance and cost.…”

Section: Introductionmentioning

confidence: 99%

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Sumesh¹,

Yearwood²,

Huda³

et al. 2022

Computers, Materials &Amp; Continua

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Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training methodology against an individual training methodology and identifying a gap between them. We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart Rate Variability (HRV) that may aid real time application. This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.

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Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care

Cited by 13 publications

References 13 publications

Application of learning algorithms in smart home IoT system security

Application of learning algorithms in smart home IoT system security

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

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