Periodic Reporting for period 5 - NOSUDEP (A Wearable Electronics Approach To Reduce Mortality in Epilepsy)
Periodo di rendicontazione: 2023-09-01 al 2024-08-31
Epilepsy is a neurological condition that affects approximately 1% of the population (or over 50 million people in the world). Europe alone is home to 6 million people that suffer from epilepsy with one new case every minute. In certain cases, healthy patients with epilepsy can die suddenly and unexpectedly. This is known as Sudden Unexpected Death in Epilepsy (SUDEP). SUDEP kills thousands of people in Europe every year. Unfortunately the mechanisms of SUDEP are not known, which makes it unpredictable Individuals with refractory tonic-clonic or complex-partial seizures are at the highest risk of SUDEP. These comprise approximately one third of the population affected by epilepsy, i.e. in the order of 2 million people in Europe.
The focus of this research project is to carry out multidisciplinary research on wearable medical technologies with the ultimate aim of helping to protect epilepsy patients from SUDEP. These technologies will have the potential to facilitate clinical research that is not possible with existing physiological monitoring systems. This could further current understanding of the mechanisms as well as individual risks factors of SUDEP, and consequently lead to specific disease management, treatment and prevention strategies targeted to the individual patient. Additionally, the research work carried out in this project will be beneficial in a number of clinical contexts, since will enable the creation of unobtrusive, easy to use wearables which can allow long term multi-modal acquisition of physiological signals, which is crucial to advance research into the mechanisms of certain conditions, the efficacy of their related interventions, and the development of new patient management strategies.
The focus of this research project is to carry out multidisciplinary research on wearable medical technologies with the ultimate aim of helping to protect epilepsy patients from SUDEP. These technologies will have the potential to facilitate clinical research that is not possible with existing physiological monitoring systems. This could further current understanding of the mechanisms as well as individual risks factors of SUDEP, and consequently lead to specific disease management, treatment and prevention strategies targeted to the individual patient. Additionally, the research work carried out in this project will be beneficial in a number of clinical contexts, since will enable the creation of unobtrusive, easy to use wearables which can allow long term multi-modal acquisition of physiological signals, which is crucial to advance research into the mechanisms of certain conditions, the efficacy of their related interventions, and the development of new patient management strategies.
In the period of time from the beginning of the project until now, the research has mainly focused on the following areas:
1- Extraction of clinically meaningful cardiac parameters from acoustic signals in the context of epilepsy; and signal processing research leading to automatic extraction of those parameters. A highlight of the work has been a novel algorithm to reliably extract heart rate from the signal obtained with a customized sensor located on the neck.
2- Investigation of all scientific and engineering challenges surrounding extraction of oxygen saturation from the neck. A highlight of this work so far has been the characterization for the first time in literature of all the artifacts affecting the PPG signal (ie the physiological signal from where oxygen saturation is obtained) when this is sensed in the neck.
3- Investigation on whether epileptic seizures can be identified or predicted from other physiological biomarkers and not just brain signals.
4- Investigation on human factors that could affect the use of wearables to prevent SUDEP.
5-Novel system architecture and integrated circuit designs for both, power harvesting and management that would allow wearables to monitor vital parameters of epilepsy patients (and others) to perpetually operate without the user having to take them off or actively having to do anything to charge them.
1- Extraction of clinically meaningful cardiac parameters from acoustic signals in the context of epilepsy; and signal processing research leading to automatic extraction of those parameters. A highlight of the work has been a novel algorithm to reliably extract heart rate from the signal obtained with a customized sensor located on the neck.
2- Investigation of all scientific and engineering challenges surrounding extraction of oxygen saturation from the neck. A highlight of this work so far has been the characterization for the first time in literature of all the artifacts affecting the PPG signal (ie the physiological signal from where oxygen saturation is obtained) when this is sensed in the neck.
3- Investigation on whether epileptic seizures can be identified or predicted from other physiological biomarkers and not just brain signals.
4- Investigation on human factors that could affect the use of wearables to prevent SUDEP.
5-Novel system architecture and integrated circuit designs for both, power harvesting and management that would allow wearables to monitor vital parameters of epilepsy patients (and others) to perpetually operate without the user having to take them off or actively having to do anything to charge them.
Some of the scientific contributions of the project, representing progress with respect to the state of the art, have been:
1-A novel algorithm for automatic Identification of fundamental and additional sounds from cardiac sounds recordings without an auxiliary signal reference; together with a methods to differentiate cardiac events corresponding to normal cycles from those which are due to abnormal activity of the heart. The proposed algorithm achieved a sensitivity of 91.79% and 89.23% for the identification of normal fundamental, S1 and S2 sounds, and a true positive rate of 81.48% for abnormal additional sounds, outperforming other algorithms in literature.
2-A novel algorithm to extract heart rate from the signal obtained with a customized sensor located on the suprasternal notch, which on real-life testing (i.e. not controlled conditions) proved to achieve an accuracy of over 94% in identification of S1 and S2 sounds, and heart rate. The performance was superior to any other algorithm in literature.
3-A novel algorithm to automatically determine heart rate variability (HRV) by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at the neck) of an adult subject, primarily intended to record breathing sounds. The instantaneous heart rate (IHR) comparisons yielded an accuracy of 95.78% and 92.35% for S1 and S2 sounds respectively. The experimental results showed, for the first time, that the proposed algorithm can provide an accurate HRV analysis for the cardiac signals recorded at the neck, using a very small sensor.
4-A method and algorithm to extract heart rate from neck PPG signals. Mean absolute error (MAE), standard deviation error (SDAE) and root-mean-square error (RMSE) resulted in 1.22 1.54 and 1.98 beats per minute (BPM), respectively. This was the first time HR automatically extracted from a sensor of this kind was presented in literature.
5-A experimental method to characterize artifacts from PPG signals sensed at the neck. This was the first time this type of work was reported in literature.
6-A novel method to extract the jugular venous pressure (JVP) from the neck using a PPG reflection based approach. Until now JVP could only be obtained using invasive approaches. We have now proven for the first time ever that it is possible to extract JVP with a small, easy to use wearable device approach. This could potentially open up new avenues for easier diagnosis of certain cardiovascular diseases.
7- A novel algorithms which demonstrates for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations.
8- A novel algorithm that is able to automatically discriminate from EEG signals whether a subject has got epilepsy or not; achieving 100% sensitivity and 98.7% specificity in classifying 267 recordings from 105 subjects.
9-Proof-of-concept design of a wearable able to extract biomarkers from PPG signals on the neck.
10- Methods of automatic sleep position detection using one wearable channel of accelerometry data sensed on the neck, with at least 98% average accuracy in all three models; memory space down to 1.765 KB and prediction time around 0.8ms.
11-Two novel classification algorithms for artifacts and apnea detection from PPG.
12-A methodology and system architecture to supply power over mid-distance, in the context of bio-sensors. Both the whole idea of the system as well as architecture are the first time presented in literature.
The research from now until the end of the project will continue focusing on extracting as much multi-modal physiological information as possible from one single sensing location in the body, and creating integrated circuits topologies that would allow the creation of custom unobtrusive wearables for single or multi-modal physiological signal acquisition, requiring very little or zero input from patients.
1-A novel algorithm for automatic Identification of fundamental and additional sounds from cardiac sounds recordings without an auxiliary signal reference; together with a methods to differentiate cardiac events corresponding to normal cycles from those which are due to abnormal activity of the heart. The proposed algorithm achieved a sensitivity of 91.79% and 89.23% for the identification of normal fundamental, S1 and S2 sounds, and a true positive rate of 81.48% for abnormal additional sounds, outperforming other algorithms in literature.
2-A novel algorithm to extract heart rate from the signal obtained with a customized sensor located on the suprasternal notch, which on real-life testing (i.e. not controlled conditions) proved to achieve an accuracy of over 94% in identification of S1 and S2 sounds, and heart rate. The performance was superior to any other algorithm in literature.
3-A novel algorithm to automatically determine heart rate variability (HRV) by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at the neck) of an adult subject, primarily intended to record breathing sounds. The instantaneous heart rate (IHR) comparisons yielded an accuracy of 95.78% and 92.35% for S1 and S2 sounds respectively. The experimental results showed, for the first time, that the proposed algorithm can provide an accurate HRV analysis for the cardiac signals recorded at the neck, using a very small sensor.
4-A method and algorithm to extract heart rate from neck PPG signals. Mean absolute error (MAE), standard deviation error (SDAE) and root-mean-square error (RMSE) resulted in 1.22 1.54 and 1.98 beats per minute (BPM), respectively. This was the first time HR automatically extracted from a sensor of this kind was presented in literature.
5-A experimental method to characterize artifacts from PPG signals sensed at the neck. This was the first time this type of work was reported in literature.
6-A novel method to extract the jugular venous pressure (JVP) from the neck using a PPG reflection based approach. Until now JVP could only be obtained using invasive approaches. We have now proven for the first time ever that it is possible to extract JVP with a small, easy to use wearable device approach. This could potentially open up new avenues for easier diagnosis of certain cardiovascular diseases.
7- A novel algorithms which demonstrates for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations.
8- A novel algorithm that is able to automatically discriminate from EEG signals whether a subject has got epilepsy or not; achieving 100% sensitivity and 98.7% specificity in classifying 267 recordings from 105 subjects.
9-Proof-of-concept design of a wearable able to extract biomarkers from PPG signals on the neck.
10- Methods of automatic sleep position detection using one wearable channel of accelerometry data sensed on the neck, with at least 98% average accuracy in all three models; memory space down to 1.765 KB and prediction time around 0.8ms.
11-Two novel classification algorithms for artifacts and apnea detection from PPG.
12-A methodology and system architecture to supply power over mid-distance, in the context of bio-sensors. Both the whole idea of the system as well as architecture are the first time presented in literature.
The research from now until the end of the project will continue focusing on extracting as much multi-modal physiological information as possible from one single sensing location in the body, and creating integrated circuits topologies that would allow the creation of custom unobtrusive wearables for single or multi-modal physiological signal acquisition, requiring very little or zero input from patients.