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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | DETECTION OF CARDIAC ARREST BY WRIST WORN SENSOR (BLOOD FLOW SENSOR). |
| Authors Name: | Y.Bhupesh , D.Srikanth |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216913
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| Published Paper Id: | IJNRD2403672 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Cardiac arrest is a critical medical emergency that demands prompt intervention to prevent fatal outcomes. Timely detection of cardiac arrest is crucial for initiating life-saving measures. In recent years, wearable health monitoring devices have gained popularity due to their potential to continuously monitor vital signs and detect anomalies. This study presents a novel approach to detecting cardiac arrest using a wrist-worn sensor. The proposed system utilizes a combination of Heart Rate data, Blood pressure and ECG signals. By using Machine learning algorithms, such as neural networks and support vector machines, are employed to train the detection model using a dataset of simulated cardiac arrest events. The model is optimized to achieve high sensitivity and specificity in distinguishing between normal cardiac activity and sudden cardiac arrest episodes. Furthermore, the system incorporates a user-friendly interface and wireless connectivity features to facilitate seamless communication with healthcare providers and emergency services. Preliminary testing of the proposed detection system demonstrates promising results in terms of accuracy and reliability. However, further validation through clinical trials involving real-world scenarios is necessary to assess its efficacy in practical settings. If successful, the wrist-worn sensor could serve as a valuable tool for early detection and intervention in cases of cardiac arrest, potentially saving numerous lives by improving response times and outcomes. |
| Keywords: | ECG, NON-INVASIVE BLOOD PRESSURE SENSOR, WRIST WORN, MACHINE LEARNING. |
| Cite Article: | "DETECTION OF CARDIAC ARREST BY WRIST WORN SENSOR (BLOOD FLOW SENSOR).", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g577-g613, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403672.pdf |
| Downloads: | 00070 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403672 Registration ID: 216913 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: g577-g613 Country: Chennai, Tamil Nadu, India Research Area: Bio Medical Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403672 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403672 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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