A Wearable Wireless Sensor System Using Machine Learning Classification to Detect Arrhythmia

Farooq, Aqeel; Seyedmahmoudian, Mehdi; Stojcevski, Alex

doi:10.1109/jsen.2021.3062395

Cited by 22 publications

(7 citation statements)

References 34 publications

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“…With the ability of wearable medical sensors to collect more and more physiological parameter data from the human body, the performance of the wearable medical sensors themselves has also gained attention, such as more flexible sensor materials, high elasticity and high scalability [48,49]. With the emergence of new materials and new processes, these improvements in wearable medical sensors appear to be more feasible.…”

Section: Intelligent Prospect Methods Of Wearable Medical Sensorsmentioning

confidence: 99%

Wearable Sensors for Vital Signs Measurement: A Survey

2022

JSAN

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With the outbreak of coronavirus disease-2019 (COVID-19) worldwide, developments in the medical field have aroused concerns within society. As science and technology develop, wearable medical sensors have become the main means of medical data acquisition. To analyze the intelligent development status of wearable medical sensors, the current work classifies and prospects the application status and functions of wireless communication wearable medical sensors, based on human physiological data acquisition in the medical field. By understanding its working principles, data acquisition modes and action modes, the work chiefly analyzes the application of wearable medical sensors in vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep detection, and reflects the key role of wearable medical sensors in human physiological data acquisition. Further exploration and prospecting are made by investigating the improvement of information security performance of wearable medical sensors, the improvement of biological adaptability and biodegradability of new materials, and the integration of wearable medical sensors and intelligence-assisted rehabilitation. The research expects to provide a reference for the intelligent development of wearable medical sensors and real-time monitoring of human health in the follow-up medical field.

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Section: Intelligent Prospect Methods Of Wearable Medical Sensorsmentioning

confidence: 99%

Wearable Sensors for Vital Signs Measurement: A Survey

2022

JSAN

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“…PSSM, which has been used as a feature vector, claims to find the evolutionary information in a query sequence. Evolutionary information can give useful features to predict ligand sites [28]. As no evolutionary information is obtained, therefore, computations are performed to achieve different pairs of amino acids in the window.…”

Section: Feature Vectormentioning

confidence: 99%

Protein active site prediction for early drug discovery and designing

Yousaf

Shehzadi

Farooq

et al. 2021

IRASE

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Adenosine triphosphate (ATP) is an energy compound present in living organisms and is required by living cells for performing operations such as replication, molecules transportation, chemical synthesis, etc. ATP connects with living cells through specialized sites called ATP-sites. ATP-sites are present in various proteins of a living cell. The life span of a cell can be controlled by controlling ATP compounds and without the provision of energy to ATP compounds, cells cannot survive. Countless diseases treatment (such as cancer, diabetes) can be possible once protein active sites are predicted. Considering the need for an algorithm that predicts ATP-sites with higher accuracy and effectiveness, this research work predicts protein ATP sites in a very novel way. Till now Position-specific scoring matrix (PSSM) along with many physicochemical properties have been used as features with deep neural networks in order to create a model that predicts the ATP-sites. To overcome this problem of complex computation, this exertion proposes k-mer feature vectors with simple machine learning (ML) models to attain the same or even better performance with less computation required. Using 2-mer as feature vectors, this research work trained and tested five different models including KNN, Conv1D, XGBoost, SVM and Random Forest. SVM gave the best performance on k-mer features. The accuracy of the created model is 96%, MCC 90% and ROC-AUC is 99%, which are the same or even better in some aspects than the state-of-the-art results. The state-of-the-art results have an accuracy of 97%, MCC 78% and ROC-AUC is 92%. One of the benefits of the created model is that it is much simpler and more accurate.

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“…In recent years, DL technique is predominantly being applied in the analysis of ECG signals to diagnose valvular heart disease [10], arrhythmia, left ventricular hypertrophy, heart failure, age, and myocardial infarction and yield good outcome. DL technique yields outstanding performance in a short period of time [11,12]. DL is sophisticated such that it consists of much better capability of feature.…”

Section: Introductionmentioning

confidence: 99%

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

Obayya¹,

Nemri²,

Alharbi³

et al. 2023

Computers, Materials &Amp; Continua

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With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Biomedical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECG Signal Classification (IBADL-BECGC) approach. To accomplish this, the proposed IBADL-BECGC model initially pre-processes the input signals. Besides, IBADL-BECGC model applies NasNet model to derive the features from test ECG signals. In addition, Improved Bat Algorithm (IBA) is employed to optimally fine-tune the hyperparameters related to NasNet approach. Finally, Extreme Learning Machine (ELM) classification algorithm is executed to perform ECG classification method. The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset. The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

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A Wearable Wireless Sensor System Using Machine Learning Classification to Detect Arrhythmia

Cited by 22 publications

References 34 publications

Wearable Sensors for Vital Signs Measurement: A Survey

Wearable Sensors for Vital Signs Measurement: A Survey

Protein active site prediction for early drug discovery and designing

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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