Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls

Muaremi, Amir; Gravenhorst, Franz; Grünerbl, Agnes; Arnrich, Bert; Tröster, Gerhard

doi:10.1007/978-3-319-11564-1_11

Cited by 55 publications

(79 citation statements)

References 16 publications

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“…The same features were used across studies, yielding 176 investigated features, with information about directionality with respect to the mood score on 155/176 (88%) of the cases. The other cases (n=21) report on accuracy and weightings by combining objective features into single evaluations, which was mostly observed in research papers with classification models [53,61,66,67]. …”

Section: Resultsmentioning

confidence: 99%

“…This includes features within sleep and voice. In particular, sleep duration was the most investigated feature (n=6), with statistically significant correlations in 4 studies Furthermore, subject was one of the less included categories, which could be due to the second-level processing required to achieve features of voice [66] or sleep durations through multiple sensors [51]. …”

Section: Discussionmentioning

confidence: 99%

“…They include accuracy measures and weightings that assist in the understanding of the individual objective features to classify mood and can investigate nonlinear interactions between multiple features and mood scores. As an example, Muaremi et al [66] used microphone features to classify mood; they achieved an F1 accuracy of 82% and discovered speaking time as the best-performing feature. By expanding to include GPS and accelerometer-related features, Abdullah et al [53] achieved an F1 accuracy of 85.5%, with the GPS feature distance achieving largest weighting.…”

Section: Discussionmentioning

confidence: 99%

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Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Rohani

Faurholt‐Jepsen

Kessing

et al. 2018

JMIR Mhealth Uhealth

168

146

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BackgroundSeveral studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.ObjectiveThe objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies.MethodsWe conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017.ResultsA total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity).ConclusionsSeveral studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Rohani

Faurholt‐Jepsen

Kessing

et al. 2018

JMIR Mhealth Uhealth

168

146

View full text Add to dashboard Cite

show abstract

“…Nevertheless, they did have accuracy measures and weightings that assist in the understanding of the individual objective features. In a study where Muaremi et al [60] used microphone features to classify mood, they achieved an F1 accuracy of 82%, and discovered speaking time as the best-performing feature. By expanding to include GPS and accelerometer-related features, Abdullah et al [54] achieved an F1 accuracy of 85.5% with the GPS feature: Distance achieving largest weighting.…”

Section: Feature Combined Modelsmentioning

confidence: 99%

“…In particular, sleep duration was the most investigated secondary feature (n = 4) with statistically significant correlations in all four studies including non-clinical samples of participants. Furthermore, subject is one of the less included primary features, which could be due to the second-level processing required to achieve secondary features of voice [60] or sleep durations from multiple sensors [52]. We also saw more creative secondary features such as the amount of time with no sound detection (speech pauses; = 0.34, p <.01) that β are only investigated in single studies but show promising results [55].…”

Section: Primary Featuresmentioning

confidence: 99%

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Rohani¹,

Faurholt‐Jepsen²,

Kessing³

et al. 2017

Preprint

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Background: A number of studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable technologies and mood symptoms in affective disorders (unipolar disorder and bipolar disorder). However, individual studies have reported on different and sometimes contradicting results and no quantitative systematic review of the correlation between objective behavioral features and mood symptoms has been published. Objective: The objectives of this systematic review were to 1) provide an overview of correlations between objective behavioral features and mood symptoms reported in the literature, and 2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help in the identification on which objective features have shown most promising results across studies. Methods: A systematic review of the scientific literature, reported according to the Preferred Reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was conducted. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, Pubmed, DBLP computer science bibliography, HTA, DARE, Scopus and Science Direct were searched and supplemented by hand examination of reference lists. The search was limited to studies published 2007-2017. Results: A total of 34 studies were eligible for the present review. They investigated 71 unique objective features divided into six different types covering 16 various sensor data inputs. Several objective features were found to have consistent correlation directionality with mood assessment (e.g., the amount of home stay, sleep duration, vigorous activity), while others showed directionality discrepancies across the studies (e.g., amount of SMS sent, time you spend between locations, frequency of smartphone screen activity).

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Speech‐based markers for posttraumatic stress disorder in US veterans

et al. 2019

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Background: The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to underand over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls.Methods: Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale.Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm. Results:The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation.Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders.Conclusions: This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required. K E Y W O R D S biomarkers, diagnostics, feature extraction, military, posttraumatic stress disorder, speechbased assessment, veterans

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Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls

Cited by 55 publications

References 16 publications

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Speech‐based markers for posttraumatic stress disorder in US veterans

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