{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T09:18:52Z","timestamp":1787822332583,"version":"build-2784847793"},"reference-count":19,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Brazilian National Council for Scientific and Technological Development","doi-asserted-by":"publisher","award":["131135\/2026-0"],"award-info":[{"award-number":["131135\/2026-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"award":["131135\/2026-0"],"award-info":[{"award-number":["131135\/2026-0"]}],"id":[{"id":"https:\/\/ror.org\/03swz6y49","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Wearable glucose monitoring demands low-power local processing, but conventional neural networks rely on energy-intensive multiply\u2013accumulate (MAC) operations that limit battery life. This study shows that a Spiking Neural Network (SNN), built on a regression-adapted Leaky Integrate-and-Fire (LIF) neuron, can estimate blood glucose from multi-frequency bioimpedance and auxiliary biosignals with clinically auditable accuracy at low computational and memory cost. Using data from 98 patients (717 measurements, eGluco3 device, Azambuja Hospital, Brusque, Brazil) evaluated by 5-fold walk-forward cross-validation under ISO 15197:2013, three main findings emerge. First, a new calibration method\u2014the Patient Fingerprint, built from each patient\u2019s first K sensor readings\u2014outperforms conventional one-hot patient encoding (14.2 \u00b1 2.6 mg\/dL vs. 15.4 \u00b1 3.3 mg\/dL mean absolute error) and, unlike one-hot, requires only these K readings rather than the patient\u2019s presence in the training set; a leave-patients-out analysis confirms that the fingerprint captures individual physiology and that unseen-patient accuracy improves with calibration depth but remains clinically insufficient (MAE 94.8\u219265.9 mg\/dL from K=3 to K=5), positioning clinical-grade cross-patient generalization on a larger cohort as the primary scaling axis. Second, the direct-injection fingerprint model reaches 100% of the samples within Consensus Error Grid Zones A+B across all validation folds (the rate-coding variant reaches 98.8%, just below the 99% Criterion B threshold), without requiring any demographic or clinical metadata; sensor history alone renders such records redundant; and Criterion A, however, stays below the 95% normative threshold, so the results support clinical safety rather than formal certification. Third, replacing the analog input encoding with a multiplication-free rate-coding scheme removes all first-layer MAC operations at a cost of 2.7 mg\/dL additional error; because the additional microticks raise the total operation count, this defines a design lever whose energy payoff is specific to neuromorphic hardware rather than a net saving on conventional microcontrollers. Together, these results demonstrate that SNNs offer a clinically auditable, self-calibrating, and memory-efficient path to continuous glucose estimation on embedded wearable devices.<\/jats:p>","DOI":"10.3390\/bios16090469","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T08:02:27Z","timestamp":1787817747000},"page":"469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Spiking Neural Network for Non-Invasive Glucose Estimation on Wearable Bioimpedance Biosensors, with a Multiplication-Free Neuromorphic Path"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3022-936X","authenticated-orcid":false,"given":"Matheus Willian","family":"Sprotte","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-4874","authenticated-orcid":false,"given":"Pedro","family":"Bertemes Filho","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s13534-024-00403-1","article-title":"Exploring the potential of spiking neural networks in biomedical applications: Advantages, limitations, and future perspectives","volume":"14","author":"Kim","year":"2024","journal-title":"Biomed. 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