Cardiac Arrhythmia Classification of ECG Signal Using Morphology and Heart Beat Rate

Senapati, Manoj Kumar; Senapati, Mrutyunjaya; Maka, Srinivasu

doi:10.1109/icacc.2014.20

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Therefore, if one of the heart rates exceeds the upper threshold or its value is lower than the lower threshold, the system alerts the medical staff. However, other papers on anomaly detection from ECG data are using inference methods based on dynamic programming techniques [ 50 ] and other mathematical operations [ 51 ] in order to detect deeper heart anomalies that may cause injuries, even when the heart rate is within safe ranges.…”

Section: Related Workmentioning

confidence: 99%

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An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

Mora

Gil

Terol

et al. 2017

Sensors

145

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The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

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Section: Related Workmentioning

confidence: 99%

“…t 6 : Anomaly detection : this task consists in inferring the corresponding user’s heart condition: healthy, heart failure, myocardial infarction, etc. In this field, there also exists intensive research work [ 50 , 51 ]. Recently, machine learning methods offer a good tradeoff between accuracy and computational cost [ 94 , 99 ].…”

Section: Case Studymentioning

confidence: 99%

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

Mora

Gil

Terol

et al. 2017

Sensors

145

View full text Add to dashboard Cite

show abstract

“…In order to achieve the above objective, a set of morphological and spectral features of the heartbeats are used (Senapati, M. K, et al, 2014), which allow to improve the achieved clustering by the unsupervised method k -means. Besides, other stages as preprocessing and segmentation of heartbeats and the sensibility evaluation of the number of groups are involved (Chen.…”

Section: Introductionmentioning

confidence: 99%

Análisis no supervisado aplicado a la detección de arritmias cardiacas

et al. 2017

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Una arritmia es una patología que consiste en la alteración de los latidos del corazón. A pesar de que el electrocardiograma de 12 derivaciones permite evaluar el comportamiento eléctrico del corazón para determinar ciertas patologías, existen algunas arritmias que son de difícil detección con este tipo de electrocardiografía. Por tanto, es necesario recurrir al uso del monitor Holter, debido a que facilita el registro de la actividad eléctrica del corazón durante largos periodos de tiempo, por lo general de 24 a 48 horas. Debido a la extensión de los registros proporcionados por el monitor, es común acudir al uso de sistemas computacionales para evaluar características diagnósticas y morfológicas de los latidos con el fin de determinar si existe algún tipo de anormalidad. Estos sistemas computacionales pueden basarse en técnicas supervisadas o no supervisadas de reconocimiento de patrones, pero teniendo en cuenta que en la primera opción el realizar una inspección visual de la gran cantidad de latidos presentes en un registro Holter, resulta ser una ardua tarea, además de implicar costos monetarios, en este trabajo se presenta el diseño de un sistema completo para la identificación de arritmias en registros Holter usando técnicas no supervisadas de reconocimiento de patrones. El sistema propuesto involucra etapas de pre-procesamiento de la señal, segmentación y caracterización de latidos, además de selección de características y agrupamiento. En este caso, la técnica utilizada es k-medias. Dichas etapas se aplican dentro del marco de una metodología basada en segmentos que mejora la detección de clases minoritarias. Asimismo, se considera criterios de inicialización que permiten mejorar las medidas de desempeño, en especial, la sensibilidad. Como resultado, se determina que usar k-medias con el criterio de inicialización máx-mín y un número de grupos igual a 12, permite obtener los mejores resultados, siendo: 99,36 %, 91,31 % y 99,16 % para exactitud, sensibilidad y especificidad, respectivamente.

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“…For the time series obeying the Gauss distribution ( , 2 ) and the length of ECG sequence is fixed. Thus, DE is calculated by Eq.…”

Section: Differential Entropymentioning

confidence: 99%

“…It is a non-invasive technique and used for the detection of a broad range of cardiac conditions like Arrhythmia, Heart rate variability, etc. [2]. Earlier research works were based on ECG signals to improve the time complexity, decrease the high amount of information lost and reduce the cardiologist's burden.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram

Raghu¹,

Meshram²

2018

IJIES

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An Electrocardiogram (ECG) is used as one of the important diagnostic tools for the detection of the health of a heart. An automatic heart abnormality identification methods sense numerous abnormalities or arrhythmia and decrease the physician's pressure as well as share their work load. In ECG analysis, the main focus is to enhance degree of accuracy and include a number of heart diseases that can be classified. In this research paper, arrhythmia classification is proposed using Hybrid features of T-wave in ECG. The classification system consists of majorly three phases, windowing technique, feature extraction and classification. In feature extraction phase various features are used such as Differential Entropy (DE), Peak-Magnitude RMS ratio, Auto Regressive feature based Yule Walker, Burgs method. In classification phase Deep Neural Network (DNN) classifier is used. This classifier categorizes the normal and abnormal signals efficiently. The experimental analysis showed that, the Hybrid features Arrhythmia classification performance of accuracy approximately 98.3%, Specificity 98.0% and Sensitivity 98.6% using MIT-BIH database.

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Cardiac Arrhythmia Classification of ECG Signal Using Morphology and Heart Beat Rate

Cited by 11 publications

References 11 publications

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

Análisis no supervisado aplicado a la detección de arritmias cardiacas

Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram

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