What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Artificial intelligence
- Statistics
Metin Akay mostly deals with Speech recognition, Heart sounds, Coronary artery disease, Wavelet transform and Internal medicine.
His Speech recognition research is multidisciplinary, incorporating perspectives in Neurophysiology, Signal processing, Algorithm, Biomedical signal and Wavelet.
Metin Akay works mostly in the field of Signal processing, limiting it down to concerns involving Time–frequency analysis and, occasionally, Electronic engineering and Accuracy and precision.
Within the field of Pattern recognition and Artificial intelligence Metin Akay studies Wavelet.
His Heart sounds research is multidisciplinary, incorporating elements of Coronary arteries and Diastole.
His Internal medicine study combines topics from a wide range of disciplines, such as Anesthesia, Bioacoustics, Feature vector and Cardiology.
His most cited work include:
- Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors (453 citations)
- Time frequency and wavelets in biomedical signal processing (331 citations)
- Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study (286 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Internal medicine, Cardiology, Neuroscience and Pattern recognition.
His Artificial intelligence study frequently links to adjacent areas such as Signal processing.
His Wavelet and Wavelet transform study are his primary interests in Pattern recognition.
His research investigates the connection between Wavelet and topics such as Speech recognition that intersect with issues in Time–frequency analysis.
His Coronary artery disease research integrates issues from Coronary arteries and Diastole.
Metin Akay has included themes like Adaptive filter, Bioacoustics, Angioplasty and Autoregressive model in his Heart sounds study.
He most often published in these fields:
- Artificial intelligence (16.60%)
- Internal medicine (16.60%)
- Cardiology (14.67%)
What were the highlights of his more recent work (between 2014-2021)?
- Ventral tegmental area (6.18%)
- Nicotine (7.34%)
- Dopamine (6.18%)
In recent papers he was focusing on the following fields of study:
Metin Akay mainly investigates Ventral tegmental area, Nicotine, Dopamine, Cancer research and Neuroscience.
Metin Akay has researched Nicotine in several fields, including Axon guidance and Regulation of gene expression.
The various areas that Metin Akay examines in his Cancer research study include Tumor microenvironment, In vitro, Chemotherapy and In vivo.
His research combines Respiratory system and Neuroscience.
His research on Endocrinology and Internal medicine is centered around Dopaminergic.
He combines subjects such as Time domain, CAD and Scalp with his study of Internal medicine.
Between 2014 and 2021, his most popular works were:
- Engineering a Brain Cancer Chip for High-throughput Drug Screening (92 citations)
- Engineering a High-Throughput 3-D In Vitro Glioblastoma Model (36 citations)
- Current and Future Challenges in Point-of-Care Technologies: A Paradigm-Shift in Affordable Global Healthcare With Personalized and Preventive Medicine (24 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Artificial intelligence
- Statistics
Metin Akay focuses on Ventral tegmental area, Dopamine, Nicotine, Neuroscience and Addiction.
His Ventral tegmental area study frequently draws connections to other fields, such as Microarray analysis techniques.
His work deals with themes such as Microarray, Patch clamp, Bioinformatics and Gene expression profiling, which intersect with Microarray analysis techniques.
Metin Akay undertakes interdisciplinary study in the fields of Neuroscience and Prenatal nicotine through his works.
His research integrates issues of Biological pathway, Reward system, microRNA, Neuroplasticity and Attention deficit hyperactivity disorder in his study of Dopaminergic.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
