BioSig Technologies (BSGM) Announces Artificial Intelligence Development Program with Technion – Israel Institute of Technology

November 16, 2021 9:52 AM EST

BioSig Technologies, Inc. (NASDAQ: BSGM), a medical technology company commercializing an innovative biomedical signal processing platform designed to improve signal fidelity and uncover the full range of ECG and intra-cardiac signals, today announced that the Company entered into a feasibility study with The Technion Research & Development Foundation Ltd.

Based in Haifa, Israel, Technion – Israel Institute of Technology is a public research university offering degrees in science, engineering, and related fields, such as medicine, industrial management, and education. Over the years, the Technion established itself as a leading academic institution in Artificial Intelligence (AI). It is currently ranked as number one in AI in Europe and 15th in the world, with 100 faculty members engaged in areas across the AI spectrum.

The feasibility program with BioSig will be led by Asst. Prof. Joachim Behar, Head of the Artificial Intelligence in Medicine Laboratory (AIMLab) at the Technion. Under the terms of the program, the ECG signals supplied by the PURE EP(tm) System, the Company’s signal processing technology for arrhythmia care, will be analyzed in the context of developing AI-powered algorithms for atrial fibrillation ablation procedures.

“Artificial Intelligence is promptly becoming an essential tool for increasing efficiency and driving value in many sectors of healthcare, but predictive insights that form the foundation for machine learning solutions are dependant on the high-quality input data. Our clinical work provides us with vast volumes of smaller, often undetectable cardiac signals that hold additional diagnostic information, and we are thrilled to partner with Prof. Behar and his Technion team to take our AI work to the next level. Their expertise in developing deep learning systems for ECG records is invaluable to this program, and we look forward to reporting on the progress of this promising new project,” commented Kenneth L. Londoner, Chairman, and CEO of BioSig Technologies, Inc.

“The laboratory for artificial intelligence in medicine (AIMLab.) at the Technion focuses on the usage of advanced signal processing and machine learning in medicine within the context of physiological time series analysis with a specific focus in cardiology where we have 10 years of expertise in processing and analysing the ECG signal. In particular, in our most recent research we have developed robust deep learning algorithms for AF diagnosis and risk prediction working on large databases - totalling over a million ECG recordings. We look forward to contributing our expertise to support leading industry in the field providing novel clinical ECG analysis tools. Our new partnership with BioSig is aligned with this desire to contribute and impact the medical field through AI powered algorithms to support clinical decision making in cardiology," commented Asst. Prof. Behar.

One in 18 Americans suffers from a cardiac arrhythmia. Atrial fibrillation is the most common arrhythmia type, affecting over 33 million people worldwide, including over 6 million in the U.S. The number of people suffering from atrial fibrillation is expected to reach 8-12 million by 20501. According to the Centers for Disease Control and Prevention (CDC), atrial fibrillation causes more than 750,000 hospitalizations in the U.S. each year, resulting in approximately $6 billion in healthcare spending annually2.

The PURE EP(tm) is an FDA 510(k) cleared non-invasive class II device that aims to drive procedural efficiency and efficacy in cardiac electrophysiology. To date, over 70 physicians have completed over 1600 patient cases with the PURE EP(tm) System. Clinical data acquired by the PURE EP(tm) System in a multi-center study at Texas Cardiac Arrhythmia Institute at St. David’s Medical Center, Mayo Clinic Jacksonville and Massachusetts General Hospital was recently published in the Journal of Cardiovascular Electrophysiology and is available electronically with open access via the Wiley Online Library. Study results showed 93% consensus across the blinded reviewers with a 75% overall improvement in intracardiac signal quality and confidence in interpreting PURE EP(tm) signals over conventional sources.

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