NIHA (Neural Intelligent Heart Analyzer) is the innovative software library that identifies heart abnormalities by analyzing ECGs.
(Neural Intelligent Heart Analyzer-Heart Failure)
Reducing the burden on medical staff performing heart failure staging.
NIHA-HF is an AI-based, standalone software that helps determine heart failure stage using electrocardiogram (ECG) data—with over 85% accuracy.
Collaborating with the University of Tokyo Hospital, SIMPLEX QUANTUM completed US patent registration of this technology designed to reduce the burden on medical staff performing heart failure staging.
The system references data from hundreds of thousands of ECGs interpreted by clinicians to determine heart failure stage. The SIMPLEX QUANTUM processor executes a software program to determine heart failure stage using deep learning methodology.
Combining advanced signal processing with deep learning.
Combining advanced signal processing with deep learning, NIHA-HF can be implemented in both standard 12-lead ECG devices and in lead I or similar wearable ECG products.
In the current diagnostic process, a clinician detects heart failure by obtaining a patient’s electrocardiogram and clinical information including heart rate and respiratory rate. Determining the stage of heart failure is an additional, often time-consuming step. NIHA-HF technology is designed to automate heart failure staging.
US patent registration followed registration in Japan and filing in countries worldwide. SIMPLEX QUANTUM has not yet received US Food and Drug Administration (FDA) clearance for marketing in the US. But with our pre-submission application to the FDA, we have initiated the regulatory pathway to market its technology as a medical device in the US.
(NIHA Atrial Fibrillation)
Using a 10-second, 1-lead ECG segment to detect Afib.
NIHA-AF is an AI-based software package developed to detect atrial fibrillation in lead 1 of an ECG.
The software combines the use of time-frequency analysis and a neural network to determine whether a 10-second ECG segment contains an AF event. By having studied over 60,000 ECG recordings, it is now able to detect the presence of AF events with 98.7% sensitivity. This software can be used for AF event detection in pre-recorded ECGs as well as for daily AF monitoring.
Stress Index (SI)
Estimating and quantifying stress levels.
Stress Index is an objective indicator to estimate and quantify stress levels using ECGs or pulse signals. Stress Index also provides a solution for stress level comparisons among individuals.
Stress resulting from physical or psychosocial stressors can influence the autonomous nervous system (ANS), where both the sympathetic and parasympathetic nervous systems are involved in the regulation of functions. Heart rate variability (HRV) is a reliable means to observe ANS, which can be an indirect biomarker for stress estimation.
SI is computed using the reciprocal of selected heart rate variability (HRV) indexes including time, frequency domain, and nonlinear features. This computation is designed to facilitate daily monitoring of stress levels for the prevention of lifestyle-related chronic diseases over the long term.
Electrocardiogram (ECG) Biometric Authentication
Precise. Personalized. Secure.
SIMPLEX QUANTUM’s ECG Biometric Authentication is an AI-based software that uses a single-lead ECG as a biometric for personal authentication.
Collaborating with the University of Aizu, SIMPLEX QUANTUM completed US patent registration of this technology designed to accurately authenticate an individual’s ECG waveform data and protect data integrity.
Authentication is essential to protect an individual’s identity, the integrity of sensitive personal data, and the ECG technology that produces the data. Biometrics have shown promise as a next-generation authentication tool because they are difficult to circumvent. ECG signals are especially conducive because they are highly personalized and challenging to counterfeit.
3 heartbeats in 3 seconds.
Our ECG Biometric Authentication Software enables fast authentication by using only 3 heartbeats within 3 seconds for precise identity authentication. Testing with a single-lead ECG device showed that the software achieved average false acceptance rate (FAR) and false rejection rate (FRR) below 10% on a group of 50 subjects. This performance is more promising on small groups of cohorts (less than 30), having obtained both FAR and FRR under 5.00%.