Electrodermal activity (EDA) is one of the ideal indicators for understanding the sympathetic nervous system. The detection and study of the human body's responses to certain external factors and their medical applications is a field of study to be explored. Thanks to this data and the development of algorithms, it is possible to detect stressful situations or monitor diseases such as epilepsy. Meanwhile, with a view to the future development of this technology, research is already underway to study its possible application in the assessment of suicide risk or for the early detection of symptoms of depression.

Currently, questionnaires and scales, including self-assessment scales, are used to assess suicide risk. In parallel, suicide risk factors (i.e. previous suicide attempts, suicidal intent, somatic illnesses, etc.) are also taken into account. Their application, however, does not offer a sufficient guarantee to accurately distinguish the severity of each individual's suicide risk.

As we have already mentioned in previous publications, the inclusion of biopotential sensors in wrist wearables represents a paradigm shift in this field. The devices are capable of storing and communicating all kinds of data continuously, which can then be analysed through artificial intelligence. The collection of EDA, ECG, respiratory rate or temperature variables through these devices is of great clinical utility. Many companies, such as Samsung, have already started to include EDA meters in their devices.

Electrodermal activity study (EDA)

The relevance of EDA for the study of psychiatry began to gain prominence in 1972 and over the years, its value has become more important due to the ease of measuring this variable even more accurately. In addition to EDA, the electrocardiogram (ECG) and respiratory rate are monitored to find indicators of emotional reactivity that may be related to suicide factors.

The organs are connected to the sympathetic nervous system, which is responsible for preparing the body for stressful situations, and the parasympathetic nervous system, which is responsible for slowing down body and metabolic activity to prepare the body for periods of rest and tranquillity. The skin is the exception to the above statement, as the sweat glands and blood vessels are exclusively innervated by the sympathetic nervous system. The electrodermal activity signal (EDA) is an electrical manifestation of the sympathetic innervation of the sweat glands [1].

EDA can be measured through skin conductance data as skin conductance is directly proportional to sweat secretion [2]. This makes skin conductance values an ideal measure of sympathetic nervous system activation.

Ideal normal habituation and electrodermal hyporreactivity in relation to the presentation of repeatedly identical non-significant stimuli. Source: [19]

Drastic changes or spikes in the EDA signal that are associated with a reaction to a stimulus are called skin conductance response (SCR).

EDA and SCR are measured in the same units, typically microsiemens (µS). The spectrum of the EDA signal is in the range of 0.045-0.15Hz, although it can increase to 0.37Hz during intense exercise [3][4]. The parameters defining the SCR are [5]:

  • Latency, i.e. the SCR peak appears between 1 and 5s after the triggering stimulus [6].
  • Amplitude; for a variation in the EDA signal to be considered SCR the amplitude must be at least 0.05µS or 0.04µS.
  • Recovery time.

Figure 1. On the left, ideal SCR, with typical parameters. Source [20]. On the right, EDA signal decomposed into tonic and phasic components. Source [15].

Skin conductance level (SCL) refers to the mean conductance obtained from the tonic component of the EDA signal and is a measure of the slow and smooth changes of the EDA signal over the measurement time. Non-specific skin conductance responses (NSSCRs) are the number of SCRs over a period of time not associated with any particular event, they are spontaneous fluctuations [5].

Clinical applications

The relationship of the EDA signal with the sympathetic nervous system makes its analysis very useful not only in the field of medicine, but also in psychology. An extensive review of the applications of EDA measurement can be found in reference [1]. The following are some of them, where wearables have been used to obtain the data:

  • A measure of a person's stress. For example: in the workplace [43], in the run-up to surgery [7], in older people [8] or people with dementia [9].
  • Monitoring of people with epilepsy [9], including analysis of seizure severity [9][10][11].
  • Identification and classification with emotions [12][13].
  • Real-time detection of drug use [14].
  • Assessment of workers' risk perception [15].

Solution proposed by Intelligent Data

Within the Wearables sector, Intelligent Data is already working on the development of an innovative solution for the market. We are developing devices adapted for use in healthcare environments with built-in electrodermal activity (EDA) meters. Together with other vital signs meters, such as EDA, ECG, respiratory rate or temperature, we are looking to create devices capable of identifying routines and facilitating their work in the healthcare sector.

In parallel, we are working on the development of algorithms to calculate possible health risks for patients:

  • Health developments, with special emphasis on deteriorating health and/or worsening health of elderly and/or chronically ill people.
  • Infection prediction and prevention.

Among our current solutions we highlight ID VitaID Vita, a Smartwatch device with the latest advances and software fully adapted to the telecare needs of residences, hospitals or other activities. The EDA measurement is an optional feature on demand of the device.

Relationship to depression screening and suicide prevention

A number of studies are currently being conducted in relation to suicide and depression, using data obtained through the EDA. In particular, we highlight a study conducted at Samsung Medical Center in Korea between December 2015 and October 2016. It involved 30 patients with major depressive disorder (MDD) and 37 healthy patients. All participants were diagnosed by specialised psychiatrists using the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

This study demonstrated, through proof-of-principle experiments, that EDA signals can be used as biomarkers for major depressive disorder. Patients with MDD and healthy patients were classified with 74% reliability using a decision tree-based algorithm.

The study was specifically designed to test the feasibility of an EDA-based classification of MDD patients. First, to increase discrimination, EDA was measured while subjects performed relaxation and stress induction tasks. Secondly, in addition to extracting EDA features for each phase, differential features representing differences in EDA between two different phases were also calculated. Thus, feature selection performed using SVM-RFE revealed that differential indicators of EDA and indicators measured during the stress and relaxation tasks were very useful for discrimination. Finally, these findings suggest that the proposed machine learning method employed here, which accounts for multiple alterations in EDA, offers great potential as an objective marker of MDD that can ultimately improve patient diagnosis and treatment.

EDOR Test

One of the main studies on the efficacy of EDA in studying its relationship to depression and suicide risk is the EDOR test. The EDOR test measures the orientation response. A process developed and modified over the years by Lars Håkan Thorell in the course of his studies at Linköping University. In 2014, on this basis, the company Emotra created a standardised EDOR system. A diagnostic method for examining patients with depression and measuring their electrical reactivity. In order to ultimately evaluate the effectiveness of this tool in identifying people with a particular risk of suicidal behaviour, Emotra initiated European multi-centre clinical studies on the relationship of EDA to suicidal tendencies in patients suffering from affective disorders. So far, more than 1573 people have participated in these trials.

EDOR test. Source: [19]

During the EDOR test, two fingers are placed on gold electrodes and a direct current of 0.5 V is passed through the epidermis according to standards. In parallel, a moderately loud tone is presented through headphones from time to time during the test. The electrodermal responses to the stimuli represent an increase in conductance due to the increased number of filled sweat ducts acting as conductors through the electrically highly resistant epidermis. The EDOR test examines the rate of habituation related to repeated neutral auditory stimuli. EDOR is an acronym that was coined by combining the terms "ElectroDermal" (ED) and "Orienting Response" (OR). It is used to identify an extremely hyperactive and extremely hyper-reactive EDA system. The EDA responses measured in the test are involuntary and cannot be intentionally activated, making it impossible to falsify the test result, which is an additional advantage of this method.

Conclusions

In short, the study of electrodermal activity indicators (EDA) is a field in constant study due to its potential benefits. The information offered by wearables and other measurement systems represents a new layer of detail for the clinical study of people's habits and routines. Thanks to these data, together with other biomarkers, it is possible to obtain new indicators that allow the preventive detection of the risk of suffering an epileptic seizure or to assess the increase or decrease in the risk of suicide in patients.

Bibliography

[1] W. Boucsein, Electrodermal Activity. 2007.
[2] C. W. Darrow, "THE RATIONALE FOR TREATING THE CHANGE IN GALVANIC SKIN RESPONSE AS A CHANGE IN CONDUCTANCE," Psychophysiology, vol. 1, no. 1, pp. 31-38, 1964, doi: https://doi.org/10.1111/j.1469-8986.1964.tb02618.x.
[3] H. F. Posada-Quintero et al., "Power Spectral Density Analysis of Electrodermal Activity for Sympathetic Function Assessment," doi: 10.1007/s10439-016-1606-6.
[4] H. F. Posada-Quintero et al., "Time-varying analysis of electrodermal activity during exercise," doi: 10.1371/journal.pone.0198328.
[5] H. F. Posada-Quintero and K. H. Chon, "Innovations in electrodermal activity data collection and signal processing: A systematic review," Sensors (Switzerland), vol. 20, no. 2. 2020, doi: 10.3390/s2002020479.
[6] W. Boucsein et al., "Publication recommendations for electrodermal measurements SOCIETY FOR PSYCHOPHYSIOLOGICAL RESEARCH AD HOC COMMITTEE ON ELECTRODERMAL MEAS-URES," 2012, doi: 10.1111/j.1469-8986.2012.01384.x.
[7] A. A. A. S. et al., "Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable," IEEE J. Biomed. Heal. Informatics, vol. 24, no. 1, pp. 92-100, 2020, doi: 10.1109/JBHI.2019.2893222.
[8] R. K. Nath, H. Thapliyal, and A. Caban-Holt, "Machine Learning Based Stress Monitoring in Older Adults Using Wearable Sensors and Cortisol as Stress Biomarker," J. Signal Process. Syst., 2021, doi: 10.1007/s11265-020-01611-5.
[9] B. Kikhia et al., "Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia," Sensors , vol. 16, no. 12. 2016, doi: 10.3390/s16121989.
[10] S. Beniczky, A. A. Arbune, J. Jeppesen, and P. Ryvlin, "Biomarkers of seizure severity derived from wearable devices," Epilepsia, vol. 61, no. S1, pp. S61-S66. S1, pp. S61-S66, Nov. 2020, doi: https://doi.org/10.1111/epi.16492.
[11] S. Vieluf et al., "Twenty-four-hour patterns in electrodermal activity recordings of patients with and without epileptic seizures," Epilepsia, vol. 62, no. 4, pp. 960-972, Apr. 2021, doi: https://doi.org/10.1111/epi.16843.
[12] A. Bhatti, B. Behinaein, D. Rodenburg, P. Hungler, and A. Etemad, Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition. ACIIW.
[13] S. Madhuri, J. Dorathi Jayasheeli, D. Malathi, and K. Senthilkumar, "Electrodermal activity (eda) based wearable device for qunatifying normal and abnormal emotions in humans," ARPN J. Eng. Appl. Sci. vol. 12, no. 12, pp. 3730-3735, 2017.
[14] S. Carreiro et al., "Real-Time Mobile Detection of Drug Use with Wearable Biosensors: A Pilot Study," J. Med. Toxicol., vol. 11, no. 1, pp. 73-79, 2015, doi: 10.1007/s13181-014-0439-7.
[15] B. Choi, H. Jebelli, and S. Lee, "Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers' perceived risk," Saf. Sci., vol. 115, pp. 110-120, 2019, doi: https://doi.org/10.1016/j.ssci.2019.01.022.
[16] C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Tr, and U. Ehlert. Ehlert, "Discriminating Stress From Cognitive Load Using a Wearable EDA Device," Technology, vol. 14, no. 2, pp. 410-417, 2010, doi: 10.1109/TITB.2009.2036164.
[17] A. Y. Kim, E. H. Jang, S. Kim, K. W. Choi, H. J. Jeon, H. Y. Yu & S. Byun , "Automatic detection of major depressive disorder using electrodermal activity".
[18] K. Wincewicz-Cichecka, T.Nasierowski , "Electrodermal activity and suicide risk assessment in patients with affective disorders".
[19] M. Sarchiapone, M. Iosue, V. Carli, M. Amore, E. Baca Garcia, A. Batra, D. Cosman, P. Courtet, G. Di Sciascio, R.Gusmao, T. Parnowski, P. Pestality, P. Saiz, J. Thome, A. Tingström, M. Wojnar, P. Zeppegno, L. H. Thorell, "EUDOR-A multi-centre research program: A naturalistic, European Multi-centre Clinical study of EDOR Test in adult primary care patients. Wojnar, P. Zeppegno, L. H. Thorell, "EUDOR-A multi-centre research program: A naturalistic, European Multi-centre Clinical study of EDOR Test in adult patients with primary depression ".
[20] C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Tr, and U. Ehlert, "DiscriminaSng Stress From CogniSve Load Using a Wearable EDA Device," Technology, vol. 14, no. 2, pp. 410-417, 2010, doi: 10.1109/TITB.2009.2036164.