Abstract
Nonlinear digital signal processing methods that address system complexity have provided useful computational tools for helping in the diagnosis and treatment of a wide range of pathologies. More specifically, nonlinear measures have been successful in characterizing patients with mental disorders such as Major Depression (MD). In this study, we propose the use of instantaneous measures of entropy, namely the inhomogeneous point-process approximate entropy (ipApEn) and the inhomogeneous point-process sample entropy (ipSampEn), to describe a novel characterization of MD patients undergoing affective elicitation. Because these measures are built within a nonlinear point-process model, they allow for the assessment of complexity in cardiovascular dynamics at each moment in time. Heartbeat dynamics were characterized from 48 healthy controls and 48 patients with MD while emotionally elicited through either neutral or arousing audiovisual stimuli. Experimental results coming from the arousing tasks show that ipApEn measures are able to instantaneously track heartbeat complexity as well as discern between healthy subjects and MD patients. Conversely, standard heart rate variability (HRV) analysis performed in both time and frequency domains did not show any statistical significance. We conclude that measures of entropy based on nonlinear point-process models might contribute to devising useful computational tools for care in mental health.
Highlights
We aimed at proving the potential of computational methods based on nonlinear digital signal processing to provide critical information that could be used for devising automated diagnostic tools that can serve mental healthcare
0.1 Healthy we have presented the application of an advanced digital signal processing methodology in order to characterize a subject experiencing a mental disorder such as Major Depression (MD)
Significant assessments can be performed considering only heartbeat dynamics elicited by time-varying emotional stimuli while estimating two recently proposed measures of complexity: inhomogeneous point-process approximate entropy (ipApEn) and ipSampEn (Valenza et al, 2014d)
Summary
Rapid developments in healthcare technology render digital signal processing crucial in revealing manifold information regarding human physiological functioning and pathological condition.Characterization of depression by instantaneous entropyTo date, most of the proposed digital processing techniques consider noninvasive biomedical signals such as the electrocardiogram (ECG), respiration activity, and body movement activity, in order to extract physiological parameters [e.g., mean heart rate (HR) and respiratory frequency] and demonstrate that they can provide clinically relevant information (He et al, 2013).Over the last two decades, ubiquitous and pervasive computing has permeated healthcare (Korhonen and Bardram, 2004) in helping to gather health-related information from wearable or embedded sensors, even in settings outside the hospital. Most of the proposed digital processing techniques consider noninvasive biomedical signals such as the electrocardiogram (ECG), respiration activity, and body movement activity, in order to extract physiological parameters [e.g., mean heart rate (HR) and respiratory frequency] and demonstrate that they can provide clinically relevant information (He et al, 2013). Mathematical modeling and digital signal processing techniques play an important role in the study of cardiovascular control physiology and heartbeat dynamics (Acharya et al, 2006). A recently proposed exemplary case having at its core the digital signal processing of heartbeat dynamics is provided by a multiparametric wearable platform for the physiological/behavioral monitoring of mood fluctuation in bipolar patients (Valenza et al, 2014a,b)
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