{"entity": "journal", "iuid": "ccd451be0cdf4601afc8d7dc6af1c825", "timestamp": "2026-08-20T20:57:46.870Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Sensors%20%28Basel%29.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Sensors%20%28Basel%29"}}, "title": "Sensors (Basel)", "issn": "1424-8220", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "081f237b612944e98d724af4296218aa", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/081f237b612944e98d724af4296218aa.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/081f237b612944e98d724af4296218aa"}}, "title": "MindReader: Unsupervised Classification of Electroencephalographic Data.", "authors": [{"family": "Rivas-Carrillo", "given": "Salvador Daniel", "initials": "SD"}, {"family": "Akkuratov", "given": "Evgeny E", "initials": "EE", "orcid": "0000-0002-2552-9512", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/abd9e4662bb543eebb0451af868bc319.json"}}, {"family": "Valdez Ruvalcaba", "given": "Hector", "initials": "H", "orcid": "0000-0001-7586-6894", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/275a375609e44c30b7148b95125c1666.json"}}, {"family": "Vargas-Sanchez", "given": "Angel", "initials": "A"}, {"family": "Komorowski", "given": "Jan", "initials": "J", "orcid": "0000-0002-0766-8789", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1dffb9dd373741069230b4f80d7e1698.json"}}, {"family": "San-Juan", "given": "Daniel", "initials": "D", "orcid": "0000-0001-6685-5851", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/586a4f336e3b4bbf8518fc79fcd1d9b8.json"}}, {"family": "Grabherr", "given": "Manfred G", "initials": "MG", "orcid": "0000-0001-8792-6508", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b7d96bfeead545498f188dae001abcef.json"}}], "type": "journal article", "published": "2023-03-09", "journal": {"title": "Sensors (Basel)", "issn": "1424-8220", "volume": "23", "issue": "6", "issn-l": null}, "abstract": "Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.", "doi": "10.3390/s23062971", "pmid": "36991682", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10057802"}, {"db": "pii", "key": "s23062971"}], "notes": [], "created": "2026-08-20T13:43:17.136Z", "modified": "2026-08-20T13:43:17.359Z"}, {"entity": "publication", "iuid": "a4c3e35555214e9baeac25ddd72cd16d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a4c3e35555214e9baeac25ddd72cd16d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a4c3e35555214e9baeac25ddd72cd16d"}}, "title": "Electrochemical Genosensing of E. coli Based on Padlock Probes and Rolling Circle Amplification.", "authors": [{"family": "Ben Aissa", "given": "Alejandra", "initials": "A"}, {"family": "Madaboosi", "given": "Narayanan", "initials": "N"}, {"family": "Nilsson", "given": "Mats", "initials": "M"}, {"family": "Pividori", "given": "Maria Isabel", "initials": "MI", "orcid": "0000-0002-5266-7873", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07b740fc3c5146ef880f477a3fbb7294.json"}}], "type": "journal article", "published": "2021-03-03", "journal": {"title": "Sensors (Basel)", "issn": "1424-8220", "volume": "21", "issue": "5", "issn-l": null}, "abstract": "Isothermal amplification techniques are emerging nowadays for the rapid and accurate detection of pathogenic bacteria in low resource settings, where many infectious diseases are endemic, and the lack of reliable power supply, trained personnel and specialized facilities pose critical barriers for timely diagnosis. This work addresses the detection of E. coli based on DNA isothermal amplification performed on magnetic particles (MPs) followed by electrochemical genosensing on disposable electrodes by square-wave voltammetry. In this approach, the bacterial DNA is preconcentrated using a target-specific magnetic probe and then amplified on the MPs by rolling circle amplification (RCA). Two different electrochemical readout methods for the RCA amplicons are tested. The first one relied on the labelling of the magnetic RCA product with a digoxigenin probe followed by the incubation with antiDIG-HRP antibody as electrochemical reporter. In the second case, the direct detection with an HRP-probe was performed. This latter strategy showed an improved analytical performance, while simultaneously avoiding the use of thermocyclers or bulky bench top equipment.", "doi": "10.3390/s21051749", "pmid": "33802488", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7959471"}, {"db": "pii", "key": "s21051749"}], "notes": [], "created": "2026-08-20T13:43:15.142Z", "modified": "2026-08-20T13:43:15.249Z"}], "created": "2026-08-20T13:43:15.217Z", "modified": "2026-08-20T13:43:15.217Z"}