Vasudeva, Shrivathsa Thokur, Rao, Shrikantha Sasihithlu, Panambur, Navin Karanth, Shettigar, Arun Kumar, Mahabala, Chakrapani, Kamath, Padmanabh, Gowdru Chandrashekarappa, Manjunath Patel, Linul, Emanoil (2022) Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals. Applied Sciences, 12 (15) 7711 doi:10.3390/app12157711
Reference Type | Journal (article/letter/editorial) | ||
---|---|---|---|
Title | Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals | ||
Journal | Applied Sciences | ||
Authors | Vasudeva, Shrivathsa Thokur | Author | |
Rao, Shrikantha Sasihithlu | Author | ||
Panambur, Navin Karanth | Author | ||
Shettigar, Arun Kumar | Author | ||
Mahabala, Chakrapani | Author | ||
Kamath, Padmanabh | Author | ||
Gowdru Chandrashekarappa, Manjunath Patel | Author | ||
Linul, Emanoil | Author | ||
Year | 2022 (July 31) | Volume | 12 |
Issue | 15 | ||
Publisher | MDPI AG | ||
DOI | doi:10.3390/app12157711Search in ResearchGate | ||
Generate Citation Formats | |||
Mindat Ref. ID | 15410316 | Long-form Identifier | mindat:1:5:15410316:3 |
GUID | 0 | ||
Full Reference | Vasudeva, Shrivathsa Thokur, Rao, Shrikantha Sasihithlu, Panambur, Navin Karanth, Shettigar, Arun Kumar, Mahabala, Chakrapani, Kamath, Padmanabh, Gowdru Chandrashekarappa, Manjunath Patel, Linul, Emanoil (2022) Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals. Applied Sciences, 12 (15) 7711 doi:10.3390/app12157711 | ||
Plain Text | Vasudeva, Shrivathsa Thokur, Rao, Shrikantha Sasihithlu, Panambur, Navin Karanth, Shettigar, Arun Kumar, Mahabala, Chakrapani, Kamath, Padmanabh, Gowdru Chandrashekarappa, Manjunath Patel, Linul, Emanoil (2022) Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals. Applied Sciences, 12 (15) 7711 doi:10.3390/app12157711 | ||
In | (2022, August) Applied Sciences Vol. 12 (15) MDPI AG |
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