Crowdfunding our health: economic risks and benefits

Crowdfunding is an expanding form of alternative financing that is gaining traction in the health sector. This article presents a typology for crowdfunded health projects and a review of the main economic benefits and risks of crowdfunding in the health market. We use evidence from a literature review, complimented by expert interviews, to extend the fundamental principles and established theories of crowdfunding to a health market context. Crowdfunded health projects can be classified into four types according to the venture's purpose and funding method. These are projects covering health expenses, fundraising health initiatives, supporting health research, or financing commercial health innovation. Crowdfunding could economically benefit the health sector by expanding market participation, drawing money and awareness to neglected health issues, improving access to funding, and fostering project accountability and social engagement. However, the economic risks of health-related crowdfunding include inefficient priority setting, heightened financial risk, inconsistent regulatory policies, intellectual property rights concerns, and fraud. Theorized crowdfunding behaviours such as signalling and herding can be observed in the market for health-related crowdfunding. Broader threats of market failure stemming from adverse selection and moral hazard also apply. Many of the discussed economic benefits and risks of crowdfunding health campaigns are shared more broadly with those of crowdfunding projects in other sectors. Where crowdfunding health care appears to diverge from theory is the negative externality inefficient priority setting may have towards achieving broader public health goals. Therefore, the market for crowdfunding health care must be economically stable, as well as designed to optimally and equitably improve public health

Determination of the respiratory rate based on spectral analysis of the PPG period variability

W artykule opisano algorytm do wyznaczania częstości oddychania na podstawie analizy widmowej sygnału reprezentującego zmienność okresu fali tętna. Falę tętna zarejestrowano za pomocą czujnika fotopletyzmograficznego (tzw. PPG) umieszczonego na placu ręki. Do przetwarzania sygnału PPG zaproponowano zastosowanie analizy falkowej. Przeprowadzono także ocenę dokładności opracowanej metody wykorzystując sygnał referencyjny, który reprezentuje przepływ powietrza w czasie wydechu.The arterial pressure waveform contains valuable information regarding the respiratory rate. This paper describes the algorithm developed for estimating the respiratory rate by analyzing the period variability of the peripheral pulse wave. To record a pulse wave at the finger, a transmissiontype photoplethysmographic sensor was used. PPG signals were collected from 10 healthy subjects during free breathing, and breath holding over a period of 3-min using a data acquisition system (Fig. 1). The reference breathing rate was determined from the airflow signal recorded simultaneously with the PPG signal (Figs. 7 and 8). Firstly, the PPG signal was detrended and denoised using the wavelet transform (Fig. 2 and 3). Based on the locations of the maximum points, all periods were detected and the tachogram was constructed. The signal representing the period variability (PPV) was obtained by interpolating the envelope of the tachogram with a cubic polynomial function (Fig. 5). Then, fluctuations extracted by the DWT from the PPV signal were segmented into 10 s intervals. Using Burg’s method, the AR model based PSD was computed for each segment. Finally, the respiratory component was detected as the maximum in the frequency band of 0.150.4 Hz (Fig. 6). The obtained results show (Fig. 9) that the proposed method allows us to monitor the respiratory rate and to detect the induced apnea with the acceptable accuracy