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The next frontier is to bring these powerful algorithms to implantable medical devices. Herein, a closed\u2010loop solution is proposed, where a cellular neural network is used to detect abnormal wavefronts and wavebrakes in cardiac signals recorded in human tissue is trained to achieve &gt;96% accuracy, &gt;92% precision, &gt;99% specificity, and &gt;93% sensitivity, when floating point precision weights are assumed. Unfortunately, the current hardware technologies for floating point precision are too bulky or energy intensive for compact standalone applications in medical implants. Emerging device technologies, such as memristors, can provide the compact and energy\u2010efficient hardware fabric to support these efforts and can be reliably embedded with existing sensor and actuator platforms in implantable devices. A distributed design that considers the hardware limitations in terms of overhead and limited bit precision is also discussed. The proposed distributed solution can be easily adapted to other medical technologies that require compact and efficient computing, like wearable devices and lab\u2010on\u2010chip platforms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/aisy.202200032","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T10:35:55Z","timestamp":1652351755000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hardware\u2010Mappable Cellular Neural Networks for Distributed Wavefront Detection in Next\u2010Generation Cardiac Implants"],"prefix":"10.1002","volume":"4","author":[{"given":"Zhuolin","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering The George Washington University  Washington DC 20052 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering The George Washington University  Washington DC 20052 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kedar","family":"Aras","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering The George Washington University  Washington DC 20052 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor R.","family":"Efimov","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering The George Washington University  Washington DC 20052 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0027-1145","authenticated-orcid":false,"given":"Gina C.","family":"Adam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering The George Washington University  Washington DC 20052 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.300.12.1423"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMSCS.2016.2553026"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacep.2020.12.014"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms4329"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-017-0038"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2012.08.1001"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1152\/physrev.00025.2003"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/32164"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","unstructured":"M.Kachuee S.Fazeli M.Sarrafzadeh in2018 IEEE Inter. 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