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Matteo Migliorini

Matteo Migliorini
Empatica · Data science

Bioengineering, PhD

About

28
Publications
8,485
Reads
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418
Citations
Citations since 2016
14 Research Items
354 Citations
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20162017201820192020202120220204060

Publications

Publications (28)
Article
The purpose of the present work is to examine, on a clinically diverse population of older adults (N=46) sleeping at home, the performance of two actigraphy-based sleep tracking algorithms (i.e., Actigraphy-based Sleep algorithm, ACT-S1 and Sadeh’s algorithm) compared to manually scored electroencephalography-based PSG (PSG-EEG). ACT-S1 allows for...
Article
We appreciate the comments from Dr. Stewart on our article,1 and agree that obstructive apnea and laryngospasm are potentially relevant factors in some sudden unexpected death in epilepsy (SUDEP) cases. We also agree that laryngospasm can occur during seizures. Well-documented cases have been rarely reported in witnessed SUDEPs.
Article
Full-text available
Objective: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods:...
Conference Paper
Full-text available
Purpose Embrace (Empatica, Inc., Boston, Massachusetts) is a wrist-worn device coupled with a smartphone-based alert system using accelerometer and electrodermal activity sensors. A machine learning classifier trained on inpatient and outpatient convulsive seizure data provides real-time alarms; its performances in real life settings have been prev...
Conference Paper
Full-text available
RATIONALE: Empatica (www.empatica.com) is working on the development of an automated comfortably wearable convulsive seizure (CS) detection system relying on accelerometer (ACC) and electrodermal activity (EDA) data (Epilepsia 2012, 53, 93-7). Using machine learning algorithms trained on generalized tonic-clonic seizures (GTCS) gathered inside Epil...
Poster
Full-text available
In this work, the performance of an automated seizure detection system based on ACM and EDA features measured from the wrist was presented using clinical data collected from a total of 53 patients having two types of seizures. The classifier we tested allows a high seizure detection rate for GTC and FOCM seizures that had never been trained by the...
Conference Paper
Parameters obtained from the heart rate variability (HRV) signal have good prognostic value in the cardiovascular disease (CVD), thus can cover a relevant role in the estimation of the risk stratification, especially when they are associated to other clinical and demographic data. In the view of home monitoring of CVD patients, the possibility of u...
Conference Paper
Full-text available
Measurement of wrist acceleration (ACM) by means of wearable devices has been exploited to automatically detect ongoing motor seizures in patients with epilepsy (Epilepsy and Behav 2011, 20, 638-641; Epilepsia 2013, 54(4), e58-e61). Nevertheless, such seizure detectors can show high false alarm rates in active patients, which might hinder their use...
Conference Paper
The aim of this study is the evaluation of the autonomic regulations during depressive stages in bipolar patients in order to test new quantitative and objective measures to detect such events. A sensorized T-shirt was used to record ECG signal and body movements during the night, from which HRV data and sleep macrostructure were estimated and anal...
Article
Full-text available
Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-lin...
Article
The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks wer...
Article
Lempel-Ziv Complexity (LZC) has been demonstrated to be a powerful complexity measure in several biomedical applications. During sleep, it is still not clear how many samples are required to ensure robustness of its estimate when computed on beat-to-beat interval series (RR). The aims of this study were: i) evaluation of the number of necessary sam...
Article
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". Objectives: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with th...
Article
The aim of this work is the creation of a completely automatic method for the extraction of informative parameters from peripheral signals recorded through a sensorized T-shirt. The acquired data belong to patients affected from bipolar disorder, and consist of RR series, body movements and activity type. The extracted features, i.e. linear and non...
Conference Paper
Full-text available
The aim of this study was the optimization of Time-Variant Autoregressive Models (TVAM) for tracking REM - non REM transitions during sleep, through the analysis of spectral indexes extracted from tachograms. A first improvement of TVAM was achieved by choosing the best typology of forgetting factor in the analysis of a tachogram obtained during a...
Conference Paper
The aim of this study is to identify parameters extracted from the Heart Rate Variability (HRV) signal that correlate to the clinical state in patients affected by bipolar disorder. 25 ECG and activity recordings from 12 patients were obtained by means of a sensorized T-shirt and the clinical state of the subjects was assessed by a psychiatrist. Fe...
Article
Full-text available
The aim of the study is to define physiological parameters and vital signs that may be related to the mood and mental status in patients affected by bipolar disorder. In particular we explored the autonomic nervous system through the analysis of the heart rate variability. Many different parameters, in the time and in the frequency domain, linear a...
Conference Paper
Full-text available
A high frequency of cardiac arrhythmias has been reported in sleep disordered patients. In order to detect the presence of arrhythmia during sleep, cardiac activity needs to be monitored. Several devices exist able to provide reliable Heart Rate Variability (HRV) measures in a minimally-intrusive way. Hence, there is the need for the development of...
Article
This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two...
Article
Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive...
Article
2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010, 3273-3276

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