HOTS

HOTS: A Scientifically Proven Algorithm

We carried out a first successful test for medical applications that demonstrated HOTS’ effectiveness in detecting atrial fibrillation based on electrocardiogram public data. 

Chronolife subsequently carried out a second test on data from 60 patients with sleep apnea, demonstrating once again the ability of HOTS to detect abnormal events such as Sleep Apnea.

HOTS predictive algorithm will continue to get smarter and better with large-scale data training in worsening heart failure (pending clinical trial across 552+ patients in Europe), and others.

Why You Should Deploy Chronolife Predictive Algorithm

Low Bandwidth, High Performance

The proliferation of IoT and IoMT has transformed every industry and how people manage their daily lives. At the same time, these connected devices bring about an explosion of data that can yield critical insights for predicting and detecting worsening events before they occur. 

The HOTS predictive algorithm is capable of analysing multiparametric data streams continuously, on small computing units such as smartphones or tablets, which greatly increases its compatibility and coverage areas for HOTS to be ported and integrated with a wide range of mobile devices and platforms for local analysis.

Versatility and End-to-End Embedded Offering

Chronolife offers the HOTS as an embedded predictive algorithm that can be easily integrated with any of your devices, platforms… that can collect data. Our team of experts will help you with everything from testing and custom configuration to implementation and ongoing maintenance. We’ll also collaborate with you in training and improving the predictive algorithm based on your unique use cases and data requirements. 

A predictive algorithm for data fusion analytics

Importance of Data Fusion Analytics for Wearable Sensors

According to a study published in Medical Engineering & Physics, wearable sensors are at the cusp of becoming truly pervasive and ubiquitous, with healthcare applications in a variety of areas including physiological monitoring, ambulatory monitoring  and falls detection. However, the richness of data available using wearable sensors presents challenges in the way that it is processed to provide accurate and relevant outputs. To fully exploit this data for the purposes of healthcare monitoring, data fusion techniques that interpret the complex multidimensional information can be employed to make inferences and improve the accuracy of the output.

Chronolife’s HOTS Technology for Multiparametric Sensor Data Analytics

Chronolife has pioneered a patented neuromorphic algorithm called HOTS (Hierarchy Of event-based Time Surfaces), a machine learning predictive algorithm capable of continuously analyzing complex, multiparametric data streams on low bandwidth such as smartphones or tablets, and detecting pattern deviations. HOTS can be ported and integrated with a wide range of mobile devices and platforms for local analysis and relevant alerts generation.

Embed Chronolife’s HOTS Into Your Devices & Health IT Programs for data fusion analytics

Chronolife offers HOTS as an embedded predictive algorithm that can be easily integrated with any of your data-collecting devices, IoT platforms, smart objects across a variety of purposes and programs:

Consumer smart objects such as smart watches, rings, and other wearable devices

Connected vehicle technology helps make driving safer

Health tracking wearable devices for pets

Athlete monitoring for training load, performance, and rehabilitation

Smart home systems for improving household wellness and safety

Internet of Medical Things-enabled MRI scanners for medical imaging

Research programs that require data fusion analytics on low bandwidth

Some specific example use cases scenarios include:

For general health monitoring, HOTS can be integrated with a plethora of connected home health devices such as connected watches, smart weights, and glucose/blood pressure monitors to synthesize and analyze physiological sensor data input from a variety of sources, in order to gather a global view of the users’ health and wellness status.

For hospitals and specialized medical care, HCPs can leverage HOTS, which can integrate patient questionnaires with their medical histories and comorbidities, to look for specific anomalies, triggers, warning signs based on deviation patterns particular to pathology cases. Chronolife end-to-end offering means that we’ll collaborate with you in customizing and training the predictive algorithm based on your patients’ unique medical use cases and data requirements agnostic of medtech devices.