## Results and Discussion
### Discussion  
A wireless neckband ultrasound Doppler system was proposed for continuous monitoring of the common carotid arteries, and its prototype was developed. From the phantom study, the developed prototype showed comparable results with a commercial rack-based ultrasound machine (Accuvix V10) in terms of the visual assessment of the spectrogram and the measured values (i.e., _PSV_ and _RI_). However, the image quality of the spectrogram from the developed prototype is worse than that from the commercial rack-based ultrasound machine. This may be caused mainly by electrical noise, a less-optimized ultrasound Doppler signal and the image processing parameters (e.g., the window size in the FFT and the thresholding value). In addition, limited image enhancement functional blocks are only implemented in the developed prototype to lower the computational complexity of the post-processing running in the application processor of the smartphone. Thus, the image quality in the developed prototype can be further enhanced by adding some image processing blocks applied in ultrasound B-mode imaging. For example, the spikes of the spectrogram shown in Figures 5b and 8 can be reduced by applying 2D median filtering (e.g., \(3\times 3\)) and 1D lateral smoothing filtering (e.g., 3-tap low-pass FIR filter). The sharpness of the spectrogram can be improved by using edge enhancement filtering (e.g., unsharp masking [29]). However, due to the limited computational resource in the smart-phone, the computational requirement must be carefully analyzed for implementing these images processing algorithms for the developed wireless neckband ultrasound Doppler system. Currently, the position of the two ultrasound transducers mounted in the neckband is adjusted to find the location of the common carotid arteries, and then the sample volume is manually positioned to obtain the proper spectrogram of the common carotid arteries. The location of the sample volume may be automatically determined by analyzing the pulsatile characteristics in the amplitudes of the complex baseband data acquired from the predetermined depths. For example, if the sample volume size is determined as 5 mm and the overlap ratio is 50%, there are seven candidates of the optimal sample volume location when the scanning depth is from 10 mm to 30 mm. The pulsatility from the seven candidate locations can be measured, and the optimal sample volume position can be determined when the pulsatile index reaches a maximum. The proposed wireless carotid neckband ultrasound Doppler system was preliminarily evaluated with phantom and in vivo experiments. For use in clinical settings, further clinical evaluation is necessary. Once the proposed wearable Doppler device is clinically validated, it can be used during coronary artery bypass surgery where the detection of embol is critical to lower the risks of stroke and brain injury. Additionally, it can be used for continuously monitoring hemodynamics in common carotid arteries to monitor intracranial pressure, which can be increased in patients with head trauma, stroke, brain tumor, or hydrocephalus. Moreover, the proposed wireless carotid neckband ultrasound Doppler system can be used as a monitoring tool for symptomatic or asymptomatic patients with cerebrovascular and/or cardiovascular diseases, as shown in Figure 10. Risk indicators can be continuously measured and sent to a server for archiving and data analysis and for predicting abnormalities in advance. Deep learning can be applied to identify proper indicators of cerebrovascular and/or cardiovascular diseases by using the Doppler spectrum archived in a patient database. Alternatively, the spectrogram of symptomatic or asymptomatic patients wearing the wireless carotid neckband ultrasound Doppler device can be fed to the server, and if a risk associated with cerebrovascular and/or cardiovascular