Sudden Cardiac Death prediction system using Hybrid classifier

Vanitha, L.; Suresh, G.; JenefarSheela, C.

doi:10.1109/ecs.2014.6892677

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…Highest disease incidence of 51.00% and lowest bulb yield of 13.04% was recorded in control (Table 1 and 2). The present findings are in agreement with findings of Vanitha and Suresh (2002) who reported that seed treatment with carbendazim resulted in lowest disease incidence (10.83%) of collar rot of brinjal caused by Sclerotium rolfsii compared to control (39.30%). The results also corroborates to the findings of Siddique et al, (2016) The present findings are also in accordance with the findings of Gupta et al, (2012) who reported that garlic was effective in managing collar rot of chickpea caused by S. rolfsii giving a good disease control of 76.6%.…”

Section: Resultssupporting

confidence: 93%

Assessment of Biocontrol Agents and Botanicals against White Rot of Onion Caused by Sclerotium rolfsii under Field Condition in Manipur

Konjengbam¹,

Devi²,

Singh³

2021

Int.J.Curr.Microbiol.App.Sci

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Section: Resultssupporting

confidence: 93%

Assessment of Biocontrol Agents and Botanicals against White Rot of Onion Caused by Sclerotium rolfsii under Field Condition in Manipur

Konjengbam¹,

Devi²,

Singh³

2021

Int.J.Curr.Microbiol.App.Sci

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“…Researchers have proposed different methods for identifying the risk of SCD using different markers in the literature. To predict SCA one hour before the VF onset, a kNN, SVM models were built [ 19 , 20 , 21 ]. Although these methods can be employed to predict SCA, the time required to extract features and the additional circuitry make them incompatible with real-time applications.…”

Section: Design and Experimental Setupmentioning

confidence: 99%

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson

Kota

Sharma

Albert

et al. 2023

Sensors

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The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA’s characteristics will be key to developing preventive strategies. Many lives could be saved if SCA’s early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician’s computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.

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“…Normally one-minute sample starting right before the event of VFib to allow minute-by-minute prediction for up to 3 hours recording [9,10,12,22]. However, Vanitha and Sheela et al [7,8] have used overlapping 10-minute sample window to analyze half an hour SDDB recordings while Devi et al [6] used four minutes sample duration for the same purpose. These variations could affect HRV analysis on the frequency domain.…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…A few preliminary studies showed that SCD can be detected as early as thirty minutes or one hour before the event. Vanitha and Sheela et al [7,8] tested HRV features on 30 minutes signals using 10 minutes overlapping samples and obtained up to 90% accuracy using SVM and Hybrid classifier. Devi et al [6] proved that there are significant differences on HRV values of normal and SCD patients after studying a total of one-hour duration of the data using HRV and Poincare plot features.…”

Section: Classification and Prediction Performancementioning

confidence: 99%

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ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

et al. 2019

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Heart disease remains the main leading cause of death globally and around 50% of the patients died due to sudden cardiac death (SCD). Early detection and prediction of SCD have become an important topic of research and it is crucial for cardiac patient’s survival. Electrocardiography (ECG) has always been the first screening method for patient with cardiac complaints and it is proven as an important predictor of SCD. ECG parameters such as RR interval, QT duration, QRS complex curve, J-point elevation and T-wave alternan are found effective in differentiating normal and SCD subjects. The objectives of this paper are to give an overview of SCD and to analyze multiple important ECG-based SCD detection and prediction models in terms of processing techniques and performance wise. Detail discussions are made in four major stages of the models developed including ECG data, signal pre-processing and processing techniques as well as classification methods. Heart rate variability (HRV) is found as an important SCD predictor as it is widely used in detecting or predicting SCD. Studies showed the possibility of SCD to be detected as early as one hour prior to the event using linear and non-linear features of HRV. Currently, up to 3 hours of analysis has been carried out. However, the best prediction models are only able to detect SCD at 6 minutes before the event with acceptable accuracy of 92.77%. A few arguments and recommendation in terms of data preparation, processing and classification techniques, as well as utilizing photoplethysmography with ECG are pointed out in this paper so that future analysis can be done with better accuracy of SCD detection accuracy.

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Sudden Cardiac Death prediction system using Hybrid classifier

Cited by 9 publications

References 20 publications

Assessment of Biocontrol Agents and Botanicals against White Rot of Onion Caused by Sclerotium rolfsii under Field Condition in Manipur

Assessment of Biocontrol Agents and Botanicals against White Rot of Onion Caused by Sclerotium rolfsii under Field Condition in Manipur

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

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