Advanced AI in Holter Analysis

Delivering "Neuralized" ECG Signals

Leveraging Gold Standard Methodologies with powerful Signal Processing Neural Networks

“The Neural Cloud Solutions team has developed a signal-processing algorithm to label electrocardiographic waveforms. This has created significant interest and activity amongst my cardiology/cardiac electrophysiology colleagues at St Paul's as it allows for more precise and automated analysis of large volumes of electrodiagnostics applied to patients over extended periods. This is critically important as cardiac wearables, including watches and clothing, become more ubiquitous and relevant in the clinical space.”

Dr. Alan Rabinowitz

Cardiologist at St. Paul’s Hospital, Vancouver

NeuralCloud Solutions Inc. is a fully owned subsidiary of AI/ML Innovations (CSE: AIML)

The MaxYield™ Platform

MaxYield™ utilizes proprietary Neural Networks to ingest a raw ECG recordings. It returns a clean, “neuralized” signal and a beat-by-beat PQRST data set. Our Neural Network understands what an ECG waveform looks like and can isolate the signal from the noise, regardless of recording length. This beat-by-beat data wall includes P, QRS, & T wave onsets, offsets, & time-series intervals.

From this clean signal, MaxYield™ can help unlock actionable insights. This includes the support in diagnosis of heart conditions, treatment of existing conditions, and metrics on overall heart performance. The value of this lies in the quick analysis of long ECG files. MaxYield™ can tabulate and highlights key features within minutes, rather than manually/semi-manually doing it.

We developed this Neural Network platform to help in the analysis process of ECG data. The nature of this Neural Network is a signal processing algorithm that has undergone extensive training on ECGs.

Typically, the design of ECG software is to recognize patterns, similar to matching photos. Our Neural Network learns and understands what an ECG wave actually looks like through ECG signal processing. From this, it is able to isolate the signal from the noise and label the P, QRS, and T Waves.

This in-depth & rapid analysis by our Neural Net helps to speed up the ECG data and Holter Monitor analysis process. This supports healthcare professionals diagnose, treat, & research heart disease faster, ultimately benefiting the patients.

MaxYield’s training methods have now been applied to other sensors expanding to PPG (photoplethysmogram) signals. We aim to explore correlations between bodily systems through multi-sensor Neuralization to discover new digital biomarkers.

Meet The Neural Cloud

Our Team

Paul Duffy

Chief Executive Officer - AI/ML Innovations

Peter Kendall

President & Chief Commercial Officer - AI/ML Innovations

Esmat Naikyar

President & Chief Product Officer - NeuralCloud Solutions

Michael Feist

Chief Technology Officer - NeuralCloud Solutions

Talwinder Punni

Chief Operating Officer - NeuralCloud Solutions

Cole Duffy

Head of Marketing - NeuralCloud Solutions

Pablo Pietropinto

Chief AI Officer - NeuralCloud Solutions

Juraj Mihalik

Chief Creative Officer - NeuralCloud Solutions

MaxYield

Our Products

MaxYield-ECG is able to map through signal noise and isolate the ECG waveform. This patented technology is delivers rapid, in-depth insights into heart health from any device regardless of noise or artifact levels. Automated P wave, QRS, and T wave detection and time-series intervals like QT, ST, and RR, lead to more accurate metrics like heart rate variability. The Neural Cloud offers research-grade ECG analysis, within minutes, for any length recording.

Apply Gold Standard methodologies using AI in ECG Signal Processing & Analysis through our MaxYield^TM platform.

MaxYield ECG

MaxYield-PPG locks onto every heartbeat—even when the signal is buried under real-world noise.
Our patented training methods find the true signal through motion shake, wrist-band slippage, ambient-light flicker, skin-tone absorption shifts, and all other external noise factors. The result is a consistently clean PPG waveform and research-grade heart-rate accuracy from any wrist-, finger-, or ear-worn sensor.

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MaxYield™ uses everyday wearables to map beat-to-beat intervals for precise HR and HRV metrics - regardless of activity or recording length.

MaxYield PPG

Origin Story: Revolutionizing Continuous ECG Monitoring

In the heart of the University of Alberta, a dedicated team of researchers were on a mission: to enhance the accuracy of continuous ECG monitoring using wearable technology.

With cardiovascular diseases affecting millions globally, the team recognized the urgent need for better cardiac monitoring solutions. Those that could operate in real-world conditions without compromising performance due to interference from noise.

The researchers delved into the realm of signal processing, focusing on the integration of accelerometers with ECG devices. They aimed to filter out the noise generated by everyday movements. This would ensure the continuous ECG data captured by wearables was as clean and reliable as possible.

However, as they honed their de-noising methodologies, they stumbled upon a surprising challenge. With the success of significant noise reduction, the process included inadvertent cancellation of some critical ECG signals.

This revelation was both a setback and a spark of inspiration. The team realized that they could not simply perform noise removal, they needed a more sophisticated approach. This needed to include understanding and retaining the vital information that ECGs provide. Inspired by these limitations, they began exploring innovative neural networks that could learn and adapt to the complexities of ECG signals.

After countless hours of research, experimentation, and collaboration, the team developed a groundbreaking algorithm tailored specifically for ECG analysis. Traditional methods rely on rigid templates and simple pattern recognition. They designed this neural network to train with the noise.

The network learned to differentiate between true ECG beats and various types of interference. It is able to ultimately recognize the subtleties of each heartbeat through signal isolation.

The breakthrough came when they successfully implemented a beat-by-beat analysis approach. This allows the algorithm to provide detailed insights into cardiac health. It meticulously analyzes each ECG signal and reveals crucial information. The signal cleaning filters out the noise allowing for heart rhythm analysis, variability, and detection of potential anomalies.

The researchers used innovative neural network technology to improve de-noising techniques and set a new standard for ECG analysis. Their solution offered a comprehensive understanding of heart function. This transforms how real-time ECG monitoring through wearables could be utilized in both clinical and everyday settings.

As they look to the future, the team is filled with excitement. They envision a world where wearables equipped with their advanced algorithms could provide real-time, actionable cardiac insights. This will help empower individuals to take control of their heart health. Through perseverance and innovation, they have taken a monumental step toward revolutionizing continuous ECG monitoring, and they are just getting started.