Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Surrel, Gregoire; Aminifar, Amir; Rincon, Francisco; Murali, Srinivasan; Atienza, David

doi:10.1109/tbcas.2018.2824659

Cited by 105 publications

(74 citation statements)

References 59 publications

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“…With the revolution of wearable technology, many researchers have significantly invested in developing and refining these novel technologies. For instance, in [20] the authors propose a wearable, accurate, and energy-efficient system for obstructive sleep apnea monitoring using a single-channel ECG. A wearable system based on four electroencephalogram (EEG) electrodes for real-time detection of epileptic seizures has been proposed in [21].…”

Section: Previous Work On Classification Techniquesmentioning

confidence: 99%

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

Sopic

Aminifar

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients' vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss.

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Section: Previous Work On Classification Techniquesmentioning

confidence: 99%

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

Sopic

Aminifar

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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“…In the final step, a machine learning algorithm, which is particularly selected and trained for the target problem, is used to extract the model and detect possible [17] follow the same structure as in Figure 1. To detect obstructive sleep apnea on wearable sensors, in [18], the exact same procedure is followed on the ECG signal. In [19], e-Glass, a wearable system based on four EEG electrodes for the detection of epileptic seizures is proposed, with the same flow depicted in Figure 1.…”

Section: Seizure Detection Systemmentioning

confidence: 99%

Self-Aware Wearable Systems in Epileptic Seizure Detection

Forooghifar

Aminifar

Alonso

2018

2018 21st Euromicro Conference on Digital System Design (DSD)

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Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance.

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“…Despite recent advances in wearable technologies, major challenges exist to fully exploit such systems. In particular, energy efficiency and scalability (i.e., according to the specific pathology characteristics of each patient) are important factors to take into account in any wearable sensor design [8] for personalized remote long-term health monitoring [5]- [7], [9]- [11]. Modern ultra-low power (ULP) platforms [12]- [16] can offer many advantages in terms of parallelization capabilities that can be exploited in biomedical applications.…”

Section: Introductionmentioning

confidence: 99%

“…• We design an online energy-efficient PAF prediction to be implemented on a single-core ULP wearable sensor INYU [9], which we personalize according to the characteristics of each patient. The optimized and personalized model allows to reduce the energy consumption and processing execution time, by considering the constraints of the wearable sensor.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

Giovanni

Valdés

Peon

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel computing capabilities that can enable prevention diagnosis and treatment of diseases in ambulatory or home-based setups. In this paper, we explore the benefits of the parallelization capabilities and computing heterogeneity of new wearable sensors in the context of a personalized online atrial fibrillation (AF) prediction method for daily monitoring. First, we apply optimizations to a single-core design to reduce energy, based on patient-specific training models. Second, we explore multi-core and memory banks configuration changes to adapt the computation and storage requirements to the characteristics of each patient. We evaluate our methodology on the Physionet Prediction Challenge (2001) publicly available database, and assess the energy consumption of single-core (ARM Cortex-M3 based) and new ultra-low power multi-core architectures (opensource RISC-V based) for next-generation of wearable platforms. Overall, our exploration at the application level highlights that a parallelization approach for personalized AF in multi-core wearable sensors enables energy savings up to 24 % with respect to single-core sensors. Moreover, including the adaptation of the memory subsystem (size and number of memory banks), in combination with deep sleep energy saving modes, can overall provide total energy savings up to 34 %, depending on the specific patient.

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Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Cited by 105 publications

References 59 publications

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

Self-Aware Wearable Systems in Epileptic Seizure Detection

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

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