Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach
Abstract
:1. Introduction
2. Methods
- Movement and/or 3-axis acceleration (Accelerometers (ACC) [g])
- Electrodermal Activity (EDA) in micro Siemens (µS)
- Blood Volume Pulse (BVP) in nanoWatt (nW)
- (1)
- Accuracy: The accuracy measure provides information about the percentage of participants that were classified correctly by the model, regardless of whether they are characterised as ASD or Rett.
- (2)
- Precision (Specificity): The precision measure offers information about how many participants the model classified with the correct diagnosis when predicting the label X, where X can be ‘ASD’ or ‘Rett’.
- (3)
- Recall (Sensitivity): The recall measure offers information about how many participants out of all participants with label X were correctly classified by the model. Precision and recall are metrics that provide label-specific information.
- (4)
- F1 Score: The F1 Score combines the precision and recall measures, since precision and recall provide different information.
2.1. Filtering and Feature Extraction
2.2. Model Learning and Prediction
- 1.
- Trees (algorithms that use decision trees):
- J48: an algorithm for building a decision tree [33].
- 2.
- Rules (algorithms that apply decision rules):
- PART: Class for generating a PART decision list. Uses separate-and-conquer. Builds a partial C4.5 decision tree in each iteration and makes the "best" leaf into a rule [34].
- 3.
- Lazy (algorithms that use lazy learning):
- LWL: Locally weighted learning. Uses an instance-based algorithm to assign instance weights. Can do classification (e.g., using naive Bayes) or regression (e.g., using linear regression) [35].
- 4.
- Meta (algorithms that apply or combine multiple algorithms (ensemble methods)):
- Bagging: an ensemble algorithm that learns base models on subsets of the training data with the purpose of reducing variance and avoiding overfitting [36].
- 5.
- Function (algorithms that estimate a function):
- Simple logistic regression: Classifier for building linear logistic regression models, with simple regression functions as base learners [37]. Note that this is analogous to linear regression, except that the dependant variable is nominal (as it is in our case) and not a measurement.
2.3. Ethics Approval and Consent to Participate
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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ASD with ID Participants | ||||
Patient No. | Age | Gender | Diagnoses | Medications |
1 | 19 | F |
| Sertraline Propranolol Vitamin D |
2 | 9 | F |
| Sertraline Omeprazole Ranitidine Melatonin Alimemazine |
3 | 16 | F |
| Aripiprazole |
4 | 17 | F |
| Clozapine Sodium valproate Aripiprazole |
5 | 13 | F |
| Aripiprazole Buspirone |
6 | 15 | F |
| Carbamazepine Topiramate Clobazam Perampanel Melatonin |
7 | 16 | F |
| Lansoprazole Levetiracetam Clobazam Lacosamide |
8 | 11 | F |
| Risperidone Carbamazepine Sertraline Melatonin Omeprazole Spironolactone Potassium |
9 | 13 | F |
| Sodium valproate Baclofen Trihexyphendyl Omeprazole Domperidone Risperidone Citalopram Propranolol |
10 | 16 | F |
| Fluoxetine Buspirone Risperidone Aripiprazole Trihexyphenidyl Cholecalciferol Lactulose Lansoprazole Levothyroxine |
Rett Participants | ||||
Patient Nr. | Age | Gender | Diagnoses | Medications |
1 | 17 | F |
| Sertraline Gaviscon Omeprazole Lactulose |
2 | 5 | F |
| Melatonin |
3 | 18 | F |
| Sodium valproate Lamotrigine Clobazam Azithromycin Buspirone Trihexyphenidyl Baclofen Ondasetron Ranitidine Sodium picosulfate |
4 | 8 | F |
| Osmotic laxatives |
5 | 9 | F |
| Sodium valproate |
6 | 20 | F |
| Sodium valproate |
7 | 9 | F |
| Ranitidine Melatonin Osmotic laxatives |
8 | 6 | F |
| None |
9 | 15 | F |
| Omeprazole Melatonin Lamotrigine Diazepam Clobazam Pizotifen |
10 | 14 | F |
| Movicol Vitamin D |
ASD Participants | |||
Activity Levels | Autonomic Function | ||
Cases | ACC | EDA | BVP |
1 | |||
2 | |||
3 | |||
4 | |||
5 | |||
6 | |||
7 | |||
8 | |||
9 | |||
10 | |||
Rett Participants | |||
Activity Levels | Autonomic Function | ||
Cases | ACC | EDA | BVP |
1 | |||
2 | |||
3 | |||
4 | |||
5 | |||
6 | |||
7 | |||
8 | |||
9 | |||
10 |
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Share and Cite
Iakovidou, N.; Lanzarini, E.; Singh, J.; Fiori, F.; Santosh, P. Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach. J. Clin. Med. 2020, 9, 2842. https://doi.org/10.3390/jcm9092842
Iakovidou N, Lanzarini E, Singh J, Fiori F, Santosh P. Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach. Journal of Clinical Medicine. 2020; 9(9):2842. https://doi.org/10.3390/jcm9092842
Chicago/Turabian StyleIakovidou, Nantia, Evamaria Lanzarini, Jatinder Singh, Federico Fiori, and Paramala Santosh. 2020. "Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach" Journal of Clinical Medicine 9, no. 9: 2842. https://doi.org/10.3390/jcm9092842