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

Abstract

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.

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

“…None of the previously developed automated algorithms for PLMS identification (Alvarez‐Estevez, ; Ferri et al., ; Huang et al., ; Moore et al., ; Stefani et al., ; Wetter et al., ) has been further validated in a different dataset or clinic from the one where it was developed. To the best of our knowledge, the algorithm developed by Ferri et al.…”

“…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).…”

Inter-movement interval as a primary stable measure of periodic limb movements of sleep

“…However, the significance of IMI in characterizing PLM is such that it warrants the effort to calculate it. At present, there are validated, automated systems available for PLM detection [11,13] that not only significantly reduce technician time for scoring records but also increase accuracy of the scoring and can automatically offer the IMI measurements.…”

“…PSG data were extracted from REMLOGIC European data format into MATLAB data structure, to be scored by the validated MATPLM1 auto-detection program (sensitivity: 95.3%, specificity: 91.7%) [11]. MATPLM1 strictly applies World Association of Sleep Medicine (WASM) criteria in order to score periodic leg movements (PLMs).…”

“…The MATPLM1 program was used to calculate PLM index, IMI, and PLM durations for all of sleep and separately for each sleep stage using the WASM criteria [11]. PI is defined for IMI in sleep as the number in a series of at least 3 with IMI ≥10 and ≤90 s divided by the total number of IMI in sleep.…”