Morphological Heart Arrhythmia Detection Using Hermitian Basis Functions and kNN Classifier

Karimifard, Saeed; Ahmadian, Alireza; Khoshnevisan, Mohammad; Nambakhsh, Mohammad Saleh

doi:10.1109/iembs.2006.260182

Cited by 39 publications

(14 citation statements)

References 6 publications

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“…It has been used in many applications in the field of data mining, statistical pattern recognition, image processing and many others. Some successful applications include recognition of handwriting [30], text classification [31] satellite imagery analysis [32] and ECG pattern analysis [33]. …”

Section: Classifiers Investigatedmentioning

confidence: 99%

Classification of Sporting Activities Using Smartphone Accelerometers

Mitchell

Monaghan

O’Connor

2013

Sensors

135

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In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach.

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Section: Classifiers Investigatedmentioning

confidence: 99%

Classification of Sporting Activities Using Smartphone Accelerometers

Mitchell

Monaghan

O’Connor

2013

Sensors

135

View full text Add to dashboard Cite

show abstract

“…Several other studies have proposed ECG detection systems based on the QRS complex. [13][14][15][16][17][18][19][20][21][22][23] These include the classification of ECG heartbeat characteristics using wavelet features [13][14][15][16][17] or waveform features. 9,18-21, 24 Karimifard et al 22 used Hermitian coefficients, and Osowski et al 23 used higher-order statistics.…”

Section: Discussionmentioning

confidence: 99%

“…28,29 Other automatic methods of heartbeat classification have been based on machine learning. 9,[13][14][15][16][17][18][19][20][21][22][23][24] Arif et al 13 and Karimifard et al 22 used the k-nearest neighbor method with good results. Ye et al 14 and Osowski et al 23 used a support vector machine, which is known to be a good tool for classification.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Classification of Heartbeats for Automated Real-Time ECG Monitoring

Park

Kang

2014

Telemedicine and e-Health

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“…These types of arrhythmias are not immediately life-threatening, but still may demand further investigation. Arrhythmias that belongs to this category can be detected from a single heartbeat, which means that shape and other morphological features define the type of the arrhythmia [18]. To perform the comparison between the HiCH and the conventional IoT-based system, we implement a similar procedure on the cloud machine which is a virtual private server (VPS) with the same OS and services.…”

Section: Setupmentioning

confidence: 99%

Empowering healthcare IoT systems with hierarchical edge-based deep learning

Azimi

Takalo-Mattila

Anzanpour

et al. 2018

Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologi

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Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.

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Morphological Heart Arrhythmia Detection Using Hermitian Basis Functions and kNN Classifier

Cited by 39 publications

References 6 publications

Classification of Sporting Activities Using Smartphone Accelerometers

Classification of Sporting Activities Using Smartphone Accelerometers

Intelligent Classification of Heartbeats for Automated Real-Time ECG Monitoring

Empowering healthcare IoT systems with hierarchical edge-based deep learning

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