Analysis of Biomedical Signals Using Differential Geometry Invariants

Studnička, Filip

doi:10.12693/aphyspola.120.a-154

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The derivative of arc length (DAL) was calculated as the difference between two adjacent samples at each sampling time. The use of arc length and Cartan curvature to study time series has already proven to be a useful tool in biosignal processing (14,15).…”

Section: Holeka Et Al: a System For Wireless Measurement Of Human Bio...mentioning

confidence: 99%

A System for Wireless Measurement of Human Biomechanics

Holeka,

Studnička,

Štěpán

et al. 2024

MMSL

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BackgroundFemoroacetabular impingement syndrome (FAI) is a complex, often post-traumatically developing impairment of the hip joint. It is characterized by ambiguous symptomatology, which makes early diagnosis difficult. AimThe study was conducted to evaluate the applicability of a triaxial gyroscopic sensor in routine practice as an additional indication criterion for operative versus conservative treatment procedures. MethodsNinety-two patients were included in the experimental retrospective study and 62 completed the examination. All patients signed informed consent. A gyroscopic sensor was placed on the right side of the pelvis above the hip joint and patients walked approximately 15 steps. Data were also evaluated while the patients climbed stairs. A complete clinical examination of the dynamics and physiological movements in the joint was performed. The data measured by the gyroscopic sensor were processed using differential geometry methods and subsequently evaluated using spectral analysis and neural networks. ResultsFAI diagnosis using gyroscopic measurement is fast and easy to implement. Our approach to processing the gyroscopic signals used to detect the stage of osteoarthritis and post-traumatic FAI could lead to more accurate detection and capture early in FAI development. ConclusionsThe obtained data are easily evaluated, interpretable, and beneficial in the diagnosis of the early stages of FAI. The results of the study show that this approach can lead to more accurate and early detection of osteoarthritis and post-traumatic FAI.

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Section: Holeka Et Al: a System For Wireless Measurement Of Human Bio...mentioning

confidence: 99%

A System for Wireless Measurement of Human Biomechanics

Holeka,

Studnička,

Štěpán

et al. 2024

MMSL

View full text Add to dashboard Cite

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Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

et al. 2022

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Osteoarthritis is the most common type of degenerative joint disease and affects millions of people. In this paper, we propose a non-obtrusive and straightforward method to assess the progression of osteoarthritis. In standard medicine praxis, osteoarthritis is observed with X-rays. In this study, we use widely available wearable sensors with gyroscopes to make the observation. Two novel methods are proposed for gyroscope data processing. A small-scale study has shown that these methods can be used to monitor osteoarthritis’s progression, and to differentiate between healthy subjects and subjects with femoroacetabular impingement syndrome.

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Novel Method for Detecting the Stage of Arthrosis and Femoroacetabular Impingement Using a Gyroscopic Sensor and Neural Networks

HOLEKA,

STUDNIČKA,

ŠTĚPÁN

et al. 2023

ACHOT

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PURPOSE OF THE STUDYFemoroacetabular impingement syndrome is a complex, often post-traumatically developing impairment of the hip joint, characterized by ambiguous symptomatology, which makes early diagnosis difficult, especially in the early stages.Experimental retrospective study was carried out to evaluate the usability of a triaxial gyroscopic sensor in routine practice as an additional indication criterion for operative versus conservative treatment procedures. MATERIAL AND METHODS92 patients were included in the retrospective study, and 62 completed the investigation. All patients signed informed consent. A gyroscopic sensor was placed on the right side of the pelvis above the hip joint, and the patients walked approximately 15 steps. Furthermore, an evaluation of the data during stair climbing and a complete clinical examination of the dynamics and physiological movements in the joint was carried out.Data measured with a gyroscopic sensor were processed using differential geometry methods and then evaluated using spectral analysis and neural networks. The proposed technique of diagnosing FAI using gyroscope measurement is a fast, easy-to-perform method. RESULTSOur approach in processing gyroscopic signals used to detect the stage of arthrosis and post-traumatically developing FAI could lead to more accurate early detection and capture in the early stages. CONCLUSIONSThe obtained data are easily evaluated, interpretable and beneficial in diagnosing the early stages of FAI. The results of the conducted research showed this approach to more accurate early detection of arthrosis and post-traumatically developing FAI.

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Analysis of Biomedical Signals Using Differential Geometry Invariants

Cited by 3 publications

References 8 publications

A System for Wireless Measurement of Human Biomechanics

A System for Wireless Measurement of Human Biomechanics

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

Novel Method for Detecting the Stage of Arthrosis and Femoroacetabular Impingement Using a Gyroscopic Sensor and Neural Networks

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