About us

The way of Seerlinq and how it works

We are able to process raw PPG data from various sources and devices to power our diagnostic algorithms.

Data Collection

PPG took the world by storm, and nowadays, it is used not only in hospitals but also in consumer wearables to monitor, e.g., your heart rate or oxygen saturation.

PPG is one of the most widespread physiological monitoring available to the general public and most of you probably already use it: in your smartwatches, fitness bands or chest straps. It is used to detect blood volume changes in the microvascular bed of tissues. It is non-invasive, low cost and in terms of basic vitals relatively easy to analyze. However, precisely medically labeled data from variety of patients and clinical settings is needed to study and analyze more complex signal patterns which can detect and monitor various cardiovascular diseases and even predict their progression.

We at Seerlinq collect medically labeled data from a great variety of cardiovascular patients and healthy individuals in different clinical settings to able to utilize PPG signal to its maximal potential. In order to guarantee the universality of our solution, we use several different devices including our dedicated HW for data collection.

Data Processing

We employ numerous steps from the world of signal processing, statistics, and machine learning to ensure the data we work with are ready for diagnosis and monitoring.

Processing of PPG data relies on signal processing algorithms and implementation. What you typically want is to have your signal clean and ready for diagnostics and health monitoring. There are many possible steps involved in signal processing, and we do them all: we apply digital filters to extract the spectral components of our interest, we denoise the data using advanced techniques such as empirical mode decomposition or auto-encoding denoisers.

We can detect artifacts or individual heart beats within the signal, we can extract features for later diagnostic models, compute derivatives and many more. Moreover, our signal processing codebase is designed in a block-wise fashion with modularity in mind, allowing for rapid prototyping and simple deployment of processing pipelines. We also employ state-of-the-art optimization algorithms to accommodate any PPG hardware.

Diagnosis and Monitoring

Seerlinq's primary mission is to deliver predictive algorithms for various cardiovascular diseases leveraging state-of-the-art machine learning methods.

Diagnosis and Monitoring

We believe that wide implementation of this solution will lead to reduced mortality, fewer hospitalizations and in-person ambulatory visits, better comfort, and overall quality of life for heart failure patients. Utilizing methods of digital and precision medicine has been furthermore associated with improved self-care, therapy adherence, reduction of depressive and anxiety symptoms and overall sensation of being in control of the disease process. On a wider scale such optimization is associated with decreased overload, improved efficiency, and cost reduction for healthcare systems.

PPG is one of the most widespread physiological monitoring available to the general public and most of you probably already use it: in your smartwatches, fitness bands or chest straps. It is used to detect blood volume changes in the microvascular bed of tissues. It is non-invasive, low cost and in terms of basic vitals relatively easy to analyze. However, precisely medically labeled data from variety of patients and clinical settings is needed to study and analyze more complex signal patterns which can detect and monitor various cardiovascular diseases and even predict their progression.

We at Seerlinq collect medically labeled data from a great variety of cardiovascular patients and healthy individuals in different clinical settings to able to utilize PPG signal to its maximal potential. In order to guarantee the universality of our solution, we use several different devices including our dedicated HW for data collection.

Processing of PPG data relies on signal processing algorithms and implementation. What you typically want is to have your signal clean and ready for diagnostics and health monitoring. There are many possible steps involved in signal processing, and we do them all: we apply digital filters to extract the spectral components of our interest, we denoise the data using advanced techniques such as empirical mode decomposition or auto-encoding denoisers.

We can detect artifacts or individual heart beats within the signal, we can extract features for later diagnostic models, compute derivatives and many more. Moreover, our signal processing codebase is designed in a block-wise fashion with modularity in mind, allowing for rapid prototyping and simple deployment of processing pipelines. We also employ state-of-the-art optimization algorithms to accommodate any PPG hardware.

During our unique proprietary research, we noticed subtle changes in photoplethysmography (PPG) signal, correlating with heart failure progression. Immediately, we realized the potential for non-invasive monitoring and prediction of disease progression in HF patients, opening the doors to the new frontiers in predictive medicine. Seerlinq transforms any device capable of acquiring PPG into a potentially powerful telemedical tool.

We believe that wide implementation of this solution will lead to reduced mortality, fewer hospitalizations and in-person ambulatory visits, better comfort, and overall quality of life for heart failure patients. Utilizing methods of digital and precision medicine has been furthermore associated with improved self-care, therapy adherence, reduction of depressive and anxiety symptoms and overall sensation of being in control of the disease process. On a wider scale such optimization is associated with decreased overload, improved efficiency, and cost reduction for healthcare systems.