Patellar reflex: I. Studies in healthy subjects

Safronov, V. A.

doi:10.1134/s036211970602006x

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Differences in stature, thigh length or stimulus strength could not explain this observation. The general absence of a correlation between stimulus size and PR latency agrees with previous studies performed on the RF (Safronov, 2006;Frijns et al, 1997). Unfortunately, similar studies for the VL have not been presented (Stam and van Crevel, 1989;Viitasalo and Komi, 1981;Moore et al, 2002).…”

Section: Latency Of the Responsesupporting

confidence: 85%

“…The CMAP appears to have a blunted shape compared to previous published results (e.g. Safronov, 2006; but see Uysal et al, 1999). Since neither the high sampling frequency (10 kHz) or filtering employed can readily account for this shape, its shape may relate to the expanded time base employed or impedance an changes that arise during limb movement.…”

Section: Description Of Raw Datacontrasting

confidence: 47%

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Surface electromyography reveals males have a slower patellar reflex than females

Vickery

Smith

2012

Journal of Electromyography and Kinesiology

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Section: Latency Of the Responsesupporting

confidence: 85%

Section: Description Of Raw Datacontrasting

confidence: 47%

Surface electromyography reveals males have a slower patellar reflex than females

Vickery

Smith

2012

Journal of Electromyography and Kinesiology

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“…The development of an objective quantification for the test is a goal that has arisen in recent years [10, 12–14]. Some work has attempted to quantify the test by performing motion analysis [15] in cerebral palsy children [16] and also proposed a new iPhone application to measure the reflex response [17].…”

Section: Introductionmentioning

confidence: 99%

Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

Salazar

López-Pérez

García-Caballero

et al. 2019

Journal of Healthcare Engineering

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Clinical evaluation of the patellar reflex is one of the most frequent diagnostic methods used by physicians and medical specialists. However, this test is usually elicited and diagnosed manually. In this work, we develop a device specifically designed to induce the patellar reflex and measure the angle and angular velocity of the leg during the course of the reflex test. We have recorded the response of 106 volunteers with the aim of finding a recognizable pattern in the responses that can allow us to classify each reflex according to the scale of the National Institute of Neurological Disorders and Stroke (NINDS). In order to elicit the patellar reflex, a hammer is attached to a specially designed pendulum, with a controlled impact force. All volunteer test subjects sit at a specific height, performing the Jendrassik maneuver during the test, and the medical staff evaluates the response in accordance with the NINDS scale. The data acquisition system is integrated by using a tapping sensor, an inertial measurement unit, a control unit, and a graphical user interface (GUI). The GUI displays the sensor behavior in real time. The sample rate is 5 kHz, and the control unit is configured for a continuous sample mode. The measured signals are processed and filtered to reduce high-frequency noise and digitally stored. After analyzing the signals, several domain-specific features are proposed to allow us to differentiate between various NINDS groups using machine learning classifiers. The results show that it is possible to automatically classify the patellar reflex into a NINDS scale using the proposed biomechanical measurements and features.

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