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This paper improves the existing Kalman-based technique for detecting electromechanical oscillations using Synchrophasor measurements. The novelty is the utilization of a distributed architecture to extract maximum a-posteriori (MAP) estimations of oscillatory parameters. This was achieved by an expectation maximization (EM) algorithm. To improve initial condition estimation, initial correlation information through a forward backward (FB) Kalman-like particle filter (KLPF) was integrated into the proposed scheme. Performance evaluation was conducted using IEEE New England 39-Bus system and Synchrophasor measurements collected from New Zealand Grid. The proposed method accurately extracted oscillatory parameters when the measurements were contaminated by continuous random small load fluctuations. The method also improved the capability of detecting multiple oscillations with similar frequencies. [ABSTRACT FROM PUBLISHER]