**6. Conclusions**

The role of clinical patient self-reporting is important yet often undocumented in literature. Health tracking technologies such as wrist-worn seizure detection devices stand to play an increasingly important role in epilepsy treatment and diagnosis as data collection tools that can help patients and caregivers to collect self-report seizure counts and other high priority health indicators for informing clinical decision-making.

In this paper, we conducted a multiphase study that included interviews with clinicians, two literature reviews, a card sorting exercise and online surveys for investigating clinical patient self-reporting needs within the context of epilepsy diagnosis and treatment. In our work with clinicians, we identified a need for more reliable mental health reporting and sleep indicators during epilepsy treatment. In our technology review, we surveyed seven types of seizure detection sensing modalities and identified a strong need for more accurate and reliable seizure reporting and motion characterization during diagnosis and treatment.

The key challenges faced by technology developers and providers are:


The findings from our research highlighted important patient self-reporting needs among a diverse set of clinicians for epilepsy diagnosis and treatment and in turn, may provide clinicians and technology developers with a useful reference for aligning development efforts with clinical information needs within epilepsy treatment. High false positives remain a problem for seizure detection devices; however low-cost hardware may be able to mitigate these issues. For example, inertial sensors [42] and the bedroom instrumented camera [49] could be sent home with patients prior to treatment; such a combination of tools could be vital information aiding neurologist in how best to treat the patient.

## **Acknowledgements**

participants [45, 62, 63, 65, 76–78]. Van De Vel et al. [65] and Narechanie et al. [64] each evaluated pressure sensing mattress inserts, however, Van De Vel et al. [65] included only 1 patient with an F-score of 0.78 while Narechanie et al. [64] included 51 patients with a

Most patients have focal types seizures (70%>50%) [80, 81]; however, only some but not all focal seizures involve limb movement. This presents a challenge as most systems to date are limited to measuring seizures based on limb movements. More reliable metrics or a combina-

To date, there has been limited work on detecting seizures using non-inertial and video sensors. Bruijne et al. analyzed [61] audio for detecting "lip smacking" and "screams" however; the performance was among the poorest of all the systems that we evaluated (F-score = 0.04). To the best of our knowledge, there are no non-EEG devices for detecting symptoms (e.g.

Inertial seizure detection wristbands [43] and nighttime video recording could provide a promising short-term solution for increasing the accuracy of patient reporting. Most patients are seen by a general practitioner and are later referred to see a neurologist [79]. This gap presents an opportunity to equip patients with data collection systems for detecting and recording patient seizures in the home prior to an initial neurology visit. For example, the open source OpenSeizureDetector [52] inertial wristband could be used in conjunction with an already available and bedroom instrumented camera such as the SAMi [49] or OpenSeizureDetector [50] detect seizures and trigger video recording. In turn, neurologists could review video for characterizing the seizure prior to treatment. Movements during seizures could be captured

Finally, seizure reporting video annotation tools could enable patients, caregivers, and neurologists to label the start and stop of seizure events could improve seizure detection performance over time and address the problem of having to manually adjust thresholds as in

The role of clinical patient self-reporting is important yet often undocumented in literature. Health tracking technologies such as wrist-worn seizure detection devices stand to play an increasingly important role in epilepsy treatment and diagnosis as data collection tools that can help patients and caregivers to collect self-report seizure counts and other high priority

In this paper, we conducted a multiphase study that included interviews with clinicians, two literature reviews, a card sorting exercise and online surveys for investigating clinical patient self-reporting needs within the context of epilepsy diagnosis and treatment. In our work with clinicians, we identified a need for more reliable mental health reporting

commercial products [42, 43] as the system will be trained for a particular individual.

health indicators for informing clinical decision-making.

tion of metrics should be studied for capturing non-motor seizure symptoms.

subtle face or hand movement during partial seizures or behavioral arrest).

perfect F-score of 1.0 for reporting seizure counts at night.

*5.2.2.3. Limited diversity of seizure types*

88 Seizures

and reviewed.

**6. Conclusions**

This chapter is taken in part from our paper on, "Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies" [24].

## **Author details**

Jonathan Bidwell1 \*, Eliana Kovich2 , Cam Escoffery<sup>3</sup> , Cherise Frazier2 and Elizabeth D. Mynatt<sup>1</sup>

