An Automatic Method for Scoring Leg Movements in Polygraphic Sleep Recordings and Its Validity in Comparison to Visual Scoring

Wetter, Thomas C.; Dirlich, Gerhard; Streit, Jonathan J.; Trenkwalder, Claudia; Schuld, Andreas; Pollmächer, Thomas

doi:10.1093/sleep/27.2.324

Cited by 22 publications

(27 citation statements)

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“…Inter‐rater and inter‐clinical variability has been evaluated on sleep staging (Danker‐Hopfe et al., ; Rosenberg & Van Hout, ; Younes et al., ) and on respiratory events (Rosenberg & Van Hout, ). Concerning motor events during sleep, previous studies have always included experts trained homogenously to evaluate inter‐rater reliability, thus excluding inter‐clinical variability (Bliwise et al., , ; Wetter et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…The visual identification of MEs is time consuming and subjective, because scoring rules are not uniquely interpretable (Iber et al., ). Studies on inter‐rater agreement have shown that, although experts tend to agree up to a level of 95% on LM identification and the PLMS index (Bliwise et al., ; Wetter et al., ), lower agreements are seen in identification of tonic and phasic EMG activities, which characterize RSWA, with identification of tonic activities being the one with the lowest agreement among experts (Bliwise et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the use of objective automated methods may help in identifying possible reasons for sleep disturbances and fragmentation. Several groups have already developed automated methods for identifying PLMS (Alvarez-Estevez, 2016;Ferri et al, 2005;Huang et al, 2015;Moore et al, 2014;Stefani, Heidbreder, Hackner, & Högl, 2017;Wetter et al, 2004), and for detecting RSWA and RBD (Ferri et al, 2008(Ferri et al, , 2010Frandsen, Nikolic, Zoetmulder, Kempfner, & Jennum, 2015;Frauscher et al, 2014;Kempfner & Nikolic, 2014;Mayer et al, 2008). Moreover, our group has recently developed a data-driven algorithm based on machine learning techniques that is able to distinguish iRBD patients from patients suffering from PLM disorder (PLMD) and healthy controls (HCs) with higher classification performances than other available automated methods (Cesari et al, 2019).…”

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confidence: 99%

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External validation of a data‐driven algorithm for muscular activity identification during sleep

Cesari

Christensen

Sørensen

et al. 2019

Journal of Sleep Research

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Several automated methods for scoring periodic limb movements during sleep (PLMS) and rapid eye movement (REM) sleep without atonia (RSWA) have been proposed, but most of them were developed and validated on data recorded in the same clinic, thus they may be biased. This work aims to validate our data‐driven algorithm for muscular activity detection during sleep, originally developed based on data recorded and manually scored at the Danish Center for Sleep Medicine. The validation was carried out on a cohort of 240 participants, including de novo Parkinson's disease (PD) patients and neurologically healthy controls, whose sleep data were recorded and manually evaluated at Paracelsus‐Elena Klinik, Kassel, Germany. In the German cohort, the algorithm showed generally good agreement between manual and automated PLMS indices, and identified with 88.75% accuracy participants with PLMS index above 15 PLMS per hour of sleep, and with 84.17% accuracy patients suffering from REM sleep behaviour disorder (RBD) showing RSWA. By comparing the algorithm performances in the Danish and German cohorts, we hypothesized that inter‐clinical differences may exist in the way limb movements are manually scored and how healthy controls are defined. Finally, the algorithm performed worse in PD patients, probably as a result of increased artefacts caused by abnormal motor events related to neurodegeneration. Our algorithm can identify, with reasonable performance, participants with RBD and increased PLMS index from data recorded in different centres, and its application may reveal inter clinical differences, which can be overcome in the future by applying automated methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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External validation of a data‐driven algorithm for muscular activity identification during sleep

Cesari

Christensen

Sørensen

et al. 2019

Journal of Sleep Research

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“…There was no effort made in this study to compare results between these, partly because each program has somewhat different concepts. The two oldest methods by Tauchmann and Pollmächer (13) and Wetter et al (14) were written in JAVA and did not use the current WASM criteria. The Ferri et al (15) was the first program to apply the basic concept of two thresholds (onset and offset of events) used in the WASM criteria, but they used fixed values for the thresholds for all records checking to ensure the resting EMG was at 2 μv for their recordings.…”

Section: Discussionmentioning

confidence: 99%

MATPLM1, A MATLAB script for scoring of periodic limb movements: preliminary validation with visual scoring

et al. 2015

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Background and Purpose A MATLAB script (MATPLM1) was developed to rigorously apply WASM scoring criteria for PLMS from bilateral EMG leg recordings. This study compares MATPLM1 with both standard technician and expert detailed visual PLMS scoring. Methods and Subjects Validation was based on a ‘macro’ level by agreement for PLMS/hr during a night recording and on a ‘micro’ level by agreement for detection of each PLMS from a stratified random sample for each subject. Data available for these analyses were from 15 RLS (age: 61.5 ±8.5, 60% female) and 9 control subjects (age: 61.4 ±7.1, 67% female) participating in another study. Results In the ‘micro’ analysis, MATPLM1 and visual detection of PLMS events agreed 87.7% for technician scoring and 94.4 % for expert scoring. The technician and MATPLM1 scoring disagreements were checked for 36 randomly selected events, 97% involved clear technician-scoring error. In the ‘macro’ analysis, MATPLM1 rates of PMLS/hr correlated highly with visual scoring by the technician (r2=0.97) and the expert scorer (r2=0.99) but the technician scoring was consistently less than MATPLM1: Median (quartiles) difference: 10 (5, 23). There was little disagreement with expert scorer [Median (quartile) difference: −0.3(−2.4, 0.3)]. Conclusions The MATPLM1 produces reliable scoring of PLMS that matches expert scoring. The standard visual scoring without careful measuring of events tends to significantly underscore PLMS. These preliminary results support the use of MATPLM1 as a preferred method of scoring PLMS for EMG recordings that are of a good quality and without significant sleep-disordered breathing events.

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“…Although the standard procedure for scoring and counting of PLMS is still based on visual criteria [11], Wetter and co-workers [12] have developed an automatic method for PLMS scoring in polysomnography, and demonstrated its high agreement with visual scoring performed by welltrained scorers. A recent study looked into the correlation between rating scales and sleep laboratory measurements [13], and showed a significant correlation between subjective (International RLS Severity Scale; IRLS) and objective (PLMS and derived measures) parameters in patients with RLS.…”

Section: Diagnostic Criteria and Instrumentsmentioning

confidence: 99%

Restless legs syndrome

Högl

Poewe

2005

Current Opinion in Neurology

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An Automatic Method for Scoring Leg Movements in Polygraphic Sleep Recordings and Its Validity in Comparison to Visual Scoring

Abstract: The degree of accordance between the 2 visual scorings and between the visual and the automatic scorings were comparable. Therefore, this method is valid and may be used for the automatic detection of leg movements in future studies.

Cited by 22 publications

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External validation of a data‐driven algorithm for muscular activity identification during sleep

External validation of a data‐driven algorithm for muscular activity identification during sleep

MATPLM1, A MATLAB script for scoring of periodic limb movements: preliminary validation with visual scoring

Restless legs syndrome

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