Time-Frequency Analysis of Accelerometry Data for Detection of Myoclonic Seizures

Nijsen, Tamara M.E.; Aarts, Ronald M.; Cluitmans, P.J.M.; Griep, P.A.M.

doi:10.1109/titb.2010.2058123

Cited by 76 publications

(52 citation statements)

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“…Many other types of features have been used and proposed for the analysis of accelerometer data. Large sets of heterogeneous features [19] and other instances inspired on frequency domain [20] or time-scale analysis [21]. Such applications focus on short-term monitoring.…”

Section: Discussionmentioning

confidence: 99%

Assessing physical activity in the daily life of cystic fibrosis patients

Dias

Gorzelniak

Jörres³

et al. 2012

Pervasive and Mobile Computing

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a b s t r a c tPhysical Activity (PA) plays an important role in the health and quality of life of cystic fibrosis (CF) patients, but little is known about their PA in daily living. With the use of accelerometers it is now possible to monitor activity profiles in detail. The goals of this study are to assess feasibility and acceptance of a longer-term use of accelerometers in daily living in CF patients, study the possibility of detecting changes in PA in relation to the patients' clinical state and compare the findings between a CF and an age-matched healthy control group. We asked 15 CF patients to wear two accelerometers for 21 days and fill in a diary. Ten of them (age 21 to 40, mean 29.5 years) participated and delivered data that could be evaluated. We also recruited 10 age-matched control subjects. Data was processed for calculating usage time and features extracted to construct models of activity. The younger patients, particularly females, were concerned with fashion and style and considered wearing the sensors a challenge. Overall, the compliance of patients with CF seemed to be lower than reported for elder subjects in the literature. Time-series analysis of the data indicated characteristic patterns of PA over time, provided that data preprocessing and noise-filtering had been optimized. Further studies have to assess whether the continuous recording of PA yields additional clinical information in CF and in particular, whether it is possible to detect or even predict exacerbations in patients with CF or other diseases.

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Section: Discussionmentioning

confidence: 99%

Assessing physical activity in the daily life of cystic fibrosis patients

Dias

Gorzelniak

Jörres³

et al. 2012

Pervasive and Mobile Computing

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“…Accelerometers have been used to measure a wide range of physiological characteristics [3][4][5][6][7], with emphasis on motor activities [8][9][10][11][12], such as walking and running [5,6,[13][14][15][16][17][18], as well as on some studies of small movements, such as those in the temporomandibular region, mainly related to jaw opening and closing movements [19][20][21], in the characterization of respiratory disorders, such as apneas [3], [22,23], in the study of human body impact and vibration [24,25], its characterization posture [26,27], and movements performed during sleep [28,29].…”

Section: Basic Concepts On Accelerometrymentioning

confidence: 99%

Accelerometry for the motion analysis of the lateral plane of the human body during gait

Côrrea

Balbinot

2011

Health Technol.

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The measurement of a particular limb in the human body is an important task in many applications. Human behavior understanding typically involves human body posture analysis or estimation, as well as the generated corresponding gestures. This behavioral characterization makes it possible to analyze, interpret and animate human actions and, therefore, enables us to use some experimental methodologies. The present aims to contribute to this field by presenting the development of an experimental measurement system for human gait using accelerometry (an accelerometer network distributed on the body). When the subject walks, the system captures and processes the events and determines the angles of the limb. For results assessment, a virtual body model is presented in real-time motion, with data being stored in a database for future evaluations and analyses. The system proposed in this paper showed an average error of 3.1°, has low cost, high accuracy and is suitable for a wide range of applications.

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“…Actigraphs, or raw accelerometry, are often used in literature and clinical settings to quantify movement for gait analysis, physical activity [21], seizure detection [112], and fall detection in the elderly [113,114]. These applications often use spectral representations within each epoch …”

Section: Quantifying Movementmentioning

confidence: 99%

“…In template matching, prior knowledge of movement patterns are exploited to identify movements by matching the pattern with a basis function. This is a common approach for identifying movements that conform to a certain pattern or behaviour, such as seizures and tremors [112,168,169]. Nijsen et al [168] developed a model to detect motor activity from epileptic patients at night.…”

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Movement characterisation with accelerometry for improved paediatric sleep assessment

Lamprecht¹

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Actigraphy is increasingly used to non-invasively estimate sleep quality in children with a suspected sleep disorder. Commercial actigraphs summarise wrist movement, conventionally measured with a uni-axial accelerometer, within a fixed epoch (typically 30s). Wake is subsequently identified as epochs of increased activity and sleep is identified as epochs of inactivity. This classification framework has some distinct limitations: actigraphy misclassifies activity during sleep as wake, and inactivity during wake (i.e. quiet rest) as sleep. In this thesis we will address these limitations by investigating three hypotheses. Firstly, uni-axial accelerometry measured solely at the wrist restricts prediction accuracy, since movements orthogonal to the measurement axis, or occurring elsewhere on the body, cannot be detected. Utilising multisite tri-axial accelerometry may consequently improve sleep and wake prediction. Secondly, there are movement characteristics that can di↵erentiate sleep from wake because the physiological nature of these movements di↵er. Identifying these characteristics may reduce false wake detections. Finally, physiological and pathological events such as apnoeas, hypopneas and transient arousals may be associated with sleep movements that contribute to false wake detections. Exploring this association may consequently explain the presence of some sleep movements.In order to address the hypotheses, 38 participants (27 male, aged 5 16 years) were recruited from children attending the sleep laboratory for suspected sleep-disordered breathing. These children were studied concurrently with polysomnography and a custom system (synchronised to within 0.1s) that recorded raw tri-axial accelerometry data (8 bit, 100Hz, ±2G) simultaneously at the left index fingertip, left wrist, upper thorax, left ankle and left great toe.The first analysis compared the accuracy of predicting sleep and wake epochs with uniaxial, tri-axial, and multisite accelerometry. Tri-axial versions of the conventional 30s epoch summaries were derived and compared to conventional uni-axial accelerometry. Multisite data were explored and verified using two feature selection algorithms with the tri-axial summaries for each accelerometer. Classification performance was significantly improved when incorporating additional accelerometers, and measuring movement with tri-axial accelerometry (Kappa agreement for multisite, tri-axial and uni-axial accelerometry: 0.565(0.231), 0.402(0.141) and 0.268(0.210), p < 0.05). Tri-axial accelerometry has clear benefits with no increase in cost or invasiveness. Although multisite accelerometry provides additional performance benefits, these benefits come at the expense of system complexity and patient discomfort.iii Moving away from epoch-by-epoch predictions, the second analysis assessed wake detection on a movement-by-movement basis. Localised spectral characteristics of raw segmented wrist movements were identified using the discrete wavelet transform. Characteristics that significantly di↵...

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Time-Frequency Analysis of Accelerometry Data for Detection of Myoclonic Seizures

Cited by 76 publications

References 10 publications

Assessing physical activity in the daily life of cystic fibrosis patients

Assessing physical activity in the daily life of cystic fibrosis patients

Accelerometry for the motion analysis of the lateral plane of the human body during gait

Movement characterisation with accelerometry for improved paediatric sleep assessment

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